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  1. Query/sql/v2/runs/v2_cli_20260502_081223_f/m8/artifacts/v2q_m8_03895c4299e6337d/cli/sql_attempt_1.metadata.json +43 -0
  2. Query/sql/v2/runs/v2_cli_20260502_081223_f/m8/artifacts/v2q_m8_03895c4299e6337d/cli/sql_attempt_2.metadata.json +43 -0
  3. Query/sql/v2/runs/v2_cli_20260502_081223_f/m8/artifacts/v2q_m8_03895c4299e6337d/cli/sql_prompt_attempt_1.txt +300 -0
  4. Query/sql/v2/runs/v2_cli_20260502_081223_f/m8/artifacts/v2q_m8_03895c4299e6337d/cli/sql_prompt_attempt_2.txt +300 -0
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  6. Query/sql/v2/runs/v2_cli_20260502_081223_f/m8/artifacts/v2q_m8_03895c4299e6337d/cli/sql_response_attempt_1.txt +4 -0
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  8. Query/sql/v2/runs/v2_cli_20260502_081223_f/m8/artifacts/v2q_m8_03895c4299e6337d/cli/sql_response_attempt_2.txt +4 -0
  9. Query/sql/v2/runs/v2_cli_20260502_081223_f/m8/artifacts/v2q_m8_03895c4299e6337d/cli/sql_stderr_attempt_1.txt +0 -0
  10. Query/sql/v2/runs/v2_cli_20260502_081223_f/m8/artifacts/v2q_m8_03895c4299e6337d/cli/sql_stderr_attempt_2.txt +0 -0
  11. Query/sql/v2/runs/v2_cli_20260502_081223_f/m8/artifacts/v2q_m8_06bd583986a0d664/cli/sql_attempt_1.metadata.json +43 -0
  12. Query/sql/v2/runs/v2_cli_20260502_081223_f/m8/artifacts/v2q_m8_06bd583986a0d664/cli/sql_attempt_2.metadata.json +43 -0
  13. Query/sql/v2/runs/v2_cli_20260502_081223_f/m8/artifacts/v2q_m8_06bd583986a0d664/cli/sql_prompt_attempt_1.txt +302 -0
  14. Query/sql/v2/runs/v2_cli_20260502_081223_f/m8/artifacts/v2q_m8_06bd583986a0d664/cli/sql_prompt_attempt_2.txt +302 -0
  15. Query/sql/v2/runs/v2_cli_20260502_081223_f/m8/artifacts/v2q_m8_06bd583986a0d664/cli/sql_response_attempt_1.raw.txt +4 -0
  16. Query/sql/v2/runs/v2_cli_20260502_081223_f/m8/artifacts/v2q_m8_06bd583986a0d664/cli/sql_response_attempt_1.txt +4 -0
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  18. Query/sql/v2/runs/v2_cli_20260502_081223_f/m8/artifacts/v2q_m8_06bd583986a0d664/cli/sql_response_attempt_2.txt +4 -0
  19. Query/sql/v2/runs/v2_cli_20260502_081223_f/m8/artifacts/v2q_m8_06bd583986a0d664/cli/sql_stderr_attempt_1.txt +0 -0
  20. Query/sql/v2/runs/v2_cli_20260502_081223_f/m8/artifacts/v2q_m8_06bd583986a0d664/cli/sql_stderr_attempt_2.txt +0 -0
  21. Query/sql/v2/runs/v2_cli_20260502_081223_f/m8/artifacts/v2q_m8_0c4031cf0e95f112/cli/sql_attempt_1.metadata.json +43 -0
  22. Query/sql/v2/runs/v2_cli_20260502_081223_f/m8/artifacts/v2q_m8_0c4031cf0e95f112/cli/sql_attempt_2.metadata.json +43 -0
  23. Query/sql/v2/runs/v2_cli_20260502_081223_f/m8/artifacts/v2q_m8_0c4031cf0e95f112/cli/sql_prompt_attempt_1.txt +302 -0
  24. Query/sql/v2/runs/v2_cli_20260502_081223_f/m8/artifacts/v2q_m8_0c4031cf0e95f112/cli/sql_prompt_attempt_2.txt +302 -0
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  29. Query/sql/v2/runs/v2_cli_20260502_081223_f/m8/artifacts/v2q_m8_0c4031cf0e95f112/cli/sql_stderr_attempt_1.txt +0 -0
  30. Query/sql/v2/runs/v2_cli_20260502_081223_f/m8/artifacts/v2q_m8_0c4031cf0e95f112/cli/sql_stderr_attempt_2.txt +0 -0
  31. Query/sql/v2/runs/v2_cli_20260502_081223_f/m8/artifacts/v2q_m8_0d9ded01382f8312/cli/sql_attempt_1.metadata.json +43 -0
  32. Query/sql/v2/runs/v2_cli_20260502_081223_f/m8/artifacts/v2q_m8_0d9ded01382f8312/cli/sql_attempt_2.metadata.json +43 -0
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  35. Query/sql/v2/runs/v2_cli_20260502_081223_f/m8/artifacts/v2q_m8_0d9ded01382f8312/cli/sql_response_attempt_1.raw.txt +4 -0
  36. Query/sql/v2/runs/v2_cli_20260502_081223_f/m8/artifacts/v2q_m8_0d9ded01382f8312/cli/sql_response_attempt_1.txt +4 -0
  37. Query/sql/v2/runs/v2_cli_20260502_081223_f/m8/artifacts/v2q_m8_0d9ded01382f8312/cli/sql_response_attempt_2.raw.txt +4 -0
  38. Query/sql/v2/runs/v2_cli_20260502_081223_f/m8/artifacts/v2q_m8_0d9ded01382f8312/cli/sql_response_attempt_2.txt +4 -0
  39. Query/sql/v2/runs/v2_cli_20260502_081223_f/m8/artifacts/v2q_m8_0d9ded01382f8312/cli/sql_stderr_attempt_1.txt +0 -0
  40. Query/sql/v2/runs/v2_cli_20260502_081223_f/m8/artifacts/v2q_m8_0d9ded01382f8312/cli/sql_stderr_attempt_2.txt +0 -0
  41. Query/sql/v2/runs/v2_cli_20260502_081223_f/m8/artifacts/v2q_m8_0e094a2304cadc3c/cli/sql_attempt_1.metadata.json +43 -0
  42. Query/sql/v2/runs/v2_cli_20260502_081223_f/m8/artifacts/v2q_m8_0e094a2304cadc3c/cli/sql_attempt_2.metadata.json +43 -0
  43. Query/sql/v2/runs/v2_cli_20260502_081223_f/m8/artifacts/v2q_m8_0e094a2304cadc3c/cli/sql_prompt_attempt_1.txt +298 -0
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+ You are generating one SQLite SELECT query for a single-table SQL QA task.
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+ Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
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+ Rules:
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+ - Use only the provided table and columns.
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+ - Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
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+ - Prefer the planned template and bound roles when provided.
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+ - Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
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+ - Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
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+ - Quote identifiers with double quotes.
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+ - Return no markdown and no extra prose.
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+
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+ Dataset context:
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+ Dataset context for SQL QA:
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+ - dataset_id: m8
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+ - dataset_name: Bank Marketing
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+ - table_name: m8
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+ - table_layout: single-table dataset (do not assume joins).
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+ - row_semantics: One row is one tabular observation with 16 feature columns and target `y`.
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+ - task_type: classification
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+ - target_column: y
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+ - main_row_count: 45211
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+ - important_fields:
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+ - age: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for age.
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+ - job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for job.
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+ - marital: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for marital.
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+ - education: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for education.
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+ - default: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for default.
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+ - balance: role=feature, type=numeric. tags=['condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for balance.
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+ - housing: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for housing.
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+ - loan: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for loan.
31
+ - contact: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for contact.
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+ - day: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for day.
33
+ - month: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for month.
34
+ - duration: role=feature, type=numeric. tags=['condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for duration.
35
+ - campaign: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for campaign.
36
+ - pdays: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for pdays.
37
+ - previous: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for previous.
38
+ - poutcome: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for poutcome.
39
+ - y: role=target, type=binary_target. tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for y.
40
+ - useful_field_combinations: [['age', 'job', 'y'], ['age', 'age', 'y']]
41
+ - fields_requiring_caution: ['y', 'balance', 'duration', 'pdays']
42
+ - source_url: https://archive.ics.uci.edu/dataset/222/bank+marketing
43
+
44
+ SQLite schema snapshot:
45
+ {
46
+ "table_name": "m8",
47
+ "quoted_table_name": "\"m8\"",
48
+ "row_count": 45211,
49
+ "columns": [
50
+ {
51
+ "name": "age",
52
+ "type": "TEXT",
53
+ "notnull": false,
54
+ "pk": false
55
+ },
56
+ {
57
+ "name": "job",
58
+ "type": "TEXT",
59
+ "notnull": false,
60
+ "pk": false
61
+ },
62
+ {
63
+ "name": "marital",
64
+ "type": "TEXT",
65
+ "notnull": false,
66
+ "pk": false
67
+ },
68
+ {
69
+ "name": "education",
70
+ "type": "TEXT",
71
+ "notnull": false,
72
+ "pk": false
73
+ },
74
+ {
75
+ "name": "default",
76
+ "type": "TEXT",
77
+ "notnull": false,
78
+ "pk": false
79
+ },
80
+ {
81
+ "name": "balance",
82
+ "type": "TEXT",
83
+ "notnull": false,
84
+ "pk": false
85
+ },
86
+ {
87
+ "name": "housing",
88
+ "type": "TEXT",
89
+ "notnull": false,
90
+ "pk": false
91
+ },
92
+ {
93
+ "name": "loan",
94
+ "type": "TEXT",
95
+ "notnull": false,
96
+ "pk": false
97
+ },
98
+ {
99
+ "name": "contact",
100
+ "type": "TEXT",
101
+ "notnull": false,
102
+ "pk": false
103
+ },
104
+ {
105
+ "name": "day",
106
+ "type": "TEXT",
107
+ "notnull": false,
108
+ "pk": false
109
+ },
110
+ {
111
+ "name": "month",
112
+ "type": "TEXT",
113
+ "notnull": false,
114
+ "pk": false
115
+ },
116
+ {
117
+ "name": "duration",
118
+ "type": "TEXT",
119
+ "notnull": false,
120
+ "pk": false
121
+ },
122
+ {
123
+ "name": "campaign",
124
+ "type": "TEXT",
125
+ "notnull": false,
126
+ "pk": false
127
+ },
128
+ {
129
+ "name": "pdays",
130
+ "type": "TEXT",
131
+ "notnull": false,
132
+ "pk": false
133
+ },
134
+ {
135
+ "name": "previous",
136
+ "type": "TEXT",
137
+ "notnull": false,
138
+ "pk": false
139
+ },
140
+ {
141
+ "name": "poutcome",
142
+ "type": "TEXT",
143
+ "notnull": false,
144
+ "pk": false
145
+ },
146
+ {
147
+ "name": "y",
148
+ "type": "TEXT",
149
+ "notnull": false,
150
+ "pk": false
151
+ }
152
+ ],
153
+ "sample_rows": [
154
+ {
155
+ "age": "58",
156
+ "job": "management",
157
+ "marital": "married",
158
+ "education": "tertiary",
159
+ "default": "no",
160
+ "balance": "2143",
161
+ "housing": "yes",
162
+ "loan": "no",
163
+ "contact": "unknown",
164
+ "day": "5",
165
+ "month": "may",
166
+ "duration": "261",
167
+ "campaign": "1",
168
+ "pdays": "-1",
169
+ "previous": "0",
170
+ "poutcome": "unknown",
171
+ "y": "no"
172
+ },
173
+ {
174
+ "age": "44",
175
+ "job": "technician",
176
+ "marital": "single",
177
+ "education": "secondary",
178
+ "default": "no",
179
+ "balance": "29",
180
+ "housing": "yes",
181
+ "loan": "no",
182
+ "contact": "unknown",
183
+ "day": "5",
184
+ "month": "may",
185
+ "duration": "151",
186
+ "campaign": "1",
187
+ "pdays": "-1",
188
+ "previous": "0",
189
+ "poutcome": "unknown",
190
+ "y": "no"
191
+ },
192
+ {
193
+ "age": "33",
194
+ "job": "entrepreneur",
195
+ "marital": "married",
196
+ "education": "secondary",
197
+ "default": "no",
198
+ "balance": "2",
199
+ "housing": "yes",
200
+ "loan": "yes",
201
+ "contact": "unknown",
202
+ "day": "5",
203
+ "month": "may",
204
+ "duration": "76",
205
+ "campaign": "1",
206
+ "pdays": "-1",
207
+ "previous": "0",
208
+ "poutcome": "unknown",
209
+ "y": "no"
210
+ },
211
+ {
212
+ "age": "47",
213
+ "job": "blue-collar",
214
+ "marital": "married",
215
+ "education": "unknown",
216
+ "default": "no",
217
+ "balance": "1506",
218
+ "housing": "yes",
219
+ "loan": "no",
220
+ "contact": "unknown",
221
+ "day": "5",
222
+ "month": "may",
223
+ "duration": "92",
224
+ "campaign": "1",
225
+ "pdays": "-1",
226
+ "previous": "0",
227
+ "poutcome": "unknown",
228
+ "y": "no"
229
+ },
230
+ {
231
+ "age": "33",
232
+ "job": "unknown",
233
+ "marital": "single",
234
+ "education": "unknown",
235
+ "default": "no",
236
+ "balance": "1",
237
+ "housing": "no",
238
+ "loan": "no",
239
+ "contact": "unknown",
240
+ "day": "5",
241
+ "month": "may",
242
+ "duration": "198",
243
+ "campaign": "1",
244
+ "pdays": "-1",
245
+ "previous": "0",
246
+ "poutcome": "unknown",
247
+ "y": "no"
248
+ }
249
+ ]
250
+ }
251
+
252
+ Shortlisted templates:
253
+ [
254
+ {
255
+ "template_id": "tpl_tpch_relative_total_threshold",
256
+ "template_name": "Relative-to-Total Extreme Threshold",
257
+ "primary_family": "tail_rarity_structure",
258
+ "portability": "partial",
259
+ "sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;",
260
+ "required_roles": [
261
+ "group_col",
262
+ "measure_col"
263
+ ]
264
+ }
265
+ ]
266
+
267
+ Problem instance:
268
+ {
269
+ "dataset_id": "m8",
270
+ "question": "Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role count_distribution. Focus on group_col=age, measure_col=pdays.",
271
+ "planned_template_id": "tpl_tpch_relative_total_threshold",
272
+ "bindings": {
273
+ "group_col": "age",
274
+ "measure_col": "pdays",
275
+ "top_k": 15,
276
+ "top_n": 7,
277
+ "num_tiles": 10,
278
+ "percentile_value": 0.95,
279
+ "z_threshold": 2.0,
280
+ "fraction_threshold": 0.05,
281
+ "baseline_multiplier": 1.75,
282
+ "baseline_fraction": 0.1,
283
+ "min_group_size": 5,
284
+ "min_support": 4,
285
+ "measure_threshold": -1.0,
286
+ "time_grain": "month",
287
+ "lookback_rows": 3,
288
+ "current_period_start": "'2024-01-01'",
289
+ "current_period_end": "'2024-04-01'",
290
+ "previous_period_start": "'2023-10-01'",
291
+ "previous_period_end": "'2024-01-01'",
292
+ "drift_ratio_threshold": 0.8
293
+ },
294
+ "can_vary": [],
295
+ "must_fix": [],
296
+ "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;"
297
+ }
298
+
299
+ Repair context:
300
+ {}
Query/sql/v2/runs/v2_cli_20260502_081223_f/m8/artifacts/v2q_m8_03895c4299e6337d/cli/sql_prompt_attempt_2.txt ADDED
@@ -0,0 +1,300 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ You are generating one SQLite SELECT query for a single-table SQL QA task.
2
+ Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
3
+ Rules:
4
+ - Use only the provided table and columns.
5
+ - Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
6
+ - Prefer the planned template and bound roles when provided.
7
+ - Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
8
+ - Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
9
+ - Quote identifiers with double quotes.
10
+ - Return no markdown and no extra prose.
11
+
12
+ Dataset context:
13
+ Dataset context for SQL QA:
14
+ - dataset_id: m8
15
+ - dataset_name: Bank Marketing
16
+ - table_name: m8
17
+ - table_layout: single-table dataset (do not assume joins).
18
+ - row_semantics: One row is one tabular observation with 16 feature columns and target `y`.
19
+ - task_type: classification
20
+ - target_column: y
21
+ - main_row_count: 45211
22
+ - important_fields:
23
+ - age: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for age.
24
+ - job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for job.
25
+ - marital: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for marital.
26
+ - education: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for education.
27
+ - default: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for default.
28
+ - balance: role=feature, type=numeric. tags=['condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for balance.
29
+ - housing: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for housing.
30
+ - loan: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for loan.
31
+ - contact: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for contact.
32
+ - day: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for day.
33
+ - month: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for month.
34
+ - duration: role=feature, type=numeric. tags=['condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for duration.
35
+ - campaign: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for campaign.
36
+ - pdays: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for pdays.
37
+ - previous: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for previous.
38
+ - poutcome: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for poutcome.
39
+ - y: role=target, type=binary_target. tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for y.
40
+ - useful_field_combinations: [['age', 'job', 'y'], ['age', 'age', 'y']]
41
+ - fields_requiring_caution: ['y', 'balance', 'duration', 'pdays']
42
+ - source_url: https://archive.ics.uci.edu/dataset/222/bank+marketing
43
+
44
+ SQLite schema snapshot:
45
+ {
46
+ "table_name": "m8",
47
+ "quoted_table_name": "\"m8\"",
48
+ "row_count": 45211,
49
+ "columns": [
50
+ {
51
+ "name": "age",
52
+ "type": "TEXT",
53
+ "notnull": false,
54
+ "pk": false
55
+ },
56
+ {
57
+ "name": "job",
58
+ "type": "TEXT",
59
+ "notnull": false,
60
+ "pk": false
61
+ },
62
+ {
63
+ "name": "marital",
64
+ "type": "TEXT",
65
+ "notnull": false,
66
+ "pk": false
67
+ },
68
+ {
69
+ "name": "education",
70
+ "type": "TEXT",
71
+ "notnull": false,
72
+ "pk": false
73
+ },
74
+ {
75
+ "name": "default",
76
+ "type": "TEXT",
77
+ "notnull": false,
78
+ "pk": false
79
+ },
80
+ {
81
+ "name": "balance",
82
+ "type": "TEXT",
83
+ "notnull": false,
84
+ "pk": false
85
+ },
86
+ {
87
+ "name": "housing",
88
+ "type": "TEXT",
89
+ "notnull": false,
90
+ "pk": false
91
+ },
92
+ {
93
+ "name": "loan",
94
+ "type": "TEXT",
95
+ "notnull": false,
96
+ "pk": false
97
+ },
98
+ {
99
+ "name": "contact",
100
+ "type": "TEXT",
101
+ "notnull": false,
102
+ "pk": false
103
+ },
104
+ {
105
+ "name": "day",
106
+ "type": "TEXT",
107
+ "notnull": false,
108
+ "pk": false
109
+ },
110
+ {
111
+ "name": "month",
112
+ "type": "TEXT",
113
+ "notnull": false,
114
+ "pk": false
115
+ },
116
+ {
117
+ "name": "duration",
118
+ "type": "TEXT",
119
+ "notnull": false,
120
+ "pk": false
121
+ },
122
+ {
123
+ "name": "campaign",
124
+ "type": "TEXT",
125
+ "notnull": false,
126
+ "pk": false
127
+ },
128
+ {
129
+ "name": "pdays",
130
+ "type": "TEXT",
131
+ "notnull": false,
132
+ "pk": false
133
+ },
134
+ {
135
+ "name": "previous",
136
+ "type": "TEXT",
137
+ "notnull": false,
138
+ "pk": false
139
+ },
140
+ {
141
+ "name": "poutcome",
142
+ "type": "TEXT",
143
+ "notnull": false,
144
+ "pk": false
145
+ },
146
+ {
147
+ "name": "y",
148
+ "type": "TEXT",
149
+ "notnull": false,
150
+ "pk": false
151
+ }
152
+ ],
153
+ "sample_rows": [
154
+ {
155
+ "age": "58",
156
+ "job": "management",
157
+ "marital": "married",
158
+ "education": "tertiary",
159
+ "default": "no",
160
+ "balance": "2143",
161
+ "housing": "yes",
162
+ "loan": "no",
163
+ "contact": "unknown",
164
+ "day": "5",
165
+ "month": "may",
166
+ "duration": "261",
167
+ "campaign": "1",
168
+ "pdays": "-1",
169
+ "previous": "0",
170
+ "poutcome": "unknown",
171
+ "y": "no"
172
+ },
173
+ {
174
+ "age": "44",
175
+ "job": "technician",
176
+ "marital": "single",
177
+ "education": "secondary",
178
+ "default": "no",
179
+ "balance": "29",
180
+ "housing": "yes",
181
+ "loan": "no",
182
+ "contact": "unknown",
183
+ "day": "5",
184
+ "month": "may",
185
+ "duration": "151",
186
+ "campaign": "1",
187
+ "pdays": "-1",
188
+ "previous": "0",
189
+ "poutcome": "unknown",
190
+ "y": "no"
191
+ },
192
+ {
193
+ "age": "33",
194
+ "job": "entrepreneur",
195
+ "marital": "married",
196
+ "education": "secondary",
197
+ "default": "no",
198
+ "balance": "2",
199
+ "housing": "yes",
200
+ "loan": "yes",
201
+ "contact": "unknown",
202
+ "day": "5",
203
+ "month": "may",
204
+ "duration": "76",
205
+ "campaign": "1",
206
+ "pdays": "-1",
207
+ "previous": "0",
208
+ "poutcome": "unknown",
209
+ "y": "no"
210
+ },
211
+ {
212
+ "age": "47",
213
+ "job": "blue-collar",
214
+ "marital": "married",
215
+ "education": "unknown",
216
+ "default": "no",
217
+ "balance": "1506",
218
+ "housing": "yes",
219
+ "loan": "no",
220
+ "contact": "unknown",
221
+ "day": "5",
222
+ "month": "may",
223
+ "duration": "92",
224
+ "campaign": "1",
225
+ "pdays": "-1",
226
+ "previous": "0",
227
+ "poutcome": "unknown",
228
+ "y": "no"
229
+ },
230
+ {
231
+ "age": "33",
232
+ "job": "unknown",
233
+ "marital": "single",
234
+ "education": "unknown",
235
+ "default": "no",
236
+ "balance": "1",
237
+ "housing": "no",
238
+ "loan": "no",
239
+ "contact": "unknown",
240
+ "day": "5",
241
+ "month": "may",
242
+ "duration": "198",
243
+ "campaign": "1",
244
+ "pdays": "-1",
245
+ "previous": "0",
246
+ "poutcome": "unknown",
247
+ "y": "no"
248
+ }
249
+ ]
250
+ }
251
+
252
+ Shortlisted templates:
253
+ [
254
+ {
255
+ "template_id": "tpl_tpch_relative_total_threshold",
256
+ "template_name": "Relative-to-Total Extreme Threshold",
257
+ "primary_family": "tail_rarity_structure",
258
+ "portability": "partial",
259
+ "sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;",
260
+ "required_roles": [
261
+ "group_col",
262
+ "measure_col"
263
+ ]
264
+ }
265
+ ]
266
+
267
+ Problem instance:
268
+ {
269
+ "dataset_id": "m8",
270
+ "question": "Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role count_distribution. Focus on group_col=age, measure_col=pdays.",
271
+ "planned_template_id": "tpl_tpch_relative_total_threshold",
272
+ "bindings": {
273
+ "group_col": "age",
274
+ "measure_col": "pdays",
275
+ "top_k": 15,
276
+ "top_n": 7,
277
+ "num_tiles": 10,
278
+ "percentile_value": 0.95,
279
+ "z_threshold": 2.0,
280
+ "fraction_threshold": 0.05,
281
+ "baseline_multiplier": 1.75,
282
+ "baseline_fraction": 0.1,
283
+ "min_group_size": 5,
284
+ "min_support": 4,
285
+ "measure_threshold": -1.0,
286
+ "time_grain": "month",
287
+ "lookback_rows": 3,
288
+ "current_period_start": "'2024-01-01'",
289
+ "current_period_end": "'2024-04-01'",
290
+ "previous_period_start": "'2023-10-01'",
291
+ "previous_period_end": "'2024-01-01'",
292
+ "drift_ratio_threshold": 0.8
293
+ },
294
+ "can_vary": [],
295
+ "must_fix": [],
296
+ "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;"
297
+ }
298
+
299
+ Repair context:
300
+ {}
Query/sql/v2/runs/v2_cli_20260502_081223_f/m8/artifacts/v2q_m8_03895c4299e6337d/cli/sql_response_attempt_1.raw.txt ADDED
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1
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2
+ {"type":"turn.started"}
3
+ {"type":"error","message":"Quota exceeded. Check your plan and billing details."}
4
+ {"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
Query/sql/v2/runs/v2_cli_20260502_081223_f/m8/artifacts/v2q_m8_03895c4299e6337d/cli/sql_response_attempt_1.txt ADDED
@@ -0,0 +1,4 @@
 
 
 
 
 
1
+ {"type":"thread.started","thread_id":"019e4108-2c5a-7843-8a2d-4db9d214b2a8"}
2
+ {"type":"turn.started"}
3
+ {"type":"error","message":"Quota exceeded. Check your plan and billing details."}
4
+ {"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
Query/sql/v2/runs/v2_cli_20260502_081223_f/m8/artifacts/v2q_m8_03895c4299e6337d/cli/sql_response_attempt_2.raw.txt ADDED
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2
+ {"type":"turn.started"}
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+ {"type":"error","message":"Quota exceeded. Check your plan and billing details."}
4
+ {"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
Query/sql/v2/runs/v2_cli_20260502_081223_f/m8/artifacts/v2q_m8_03895c4299e6337d/cli/sql_response_attempt_2.txt ADDED
@@ -0,0 +1,4 @@
 
 
 
 
 
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+ {"type":"thread.started","thread_id":"019e4108-3c73-7621-af9a-af7e7bdce4be"}
2
+ {"type":"turn.started"}
3
+ {"type":"error","message":"Quota exceeded. Check your plan and billing details."}
4
+ {"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
Query/sql/v2/runs/v2_cli_20260502_081223_f/m8/artifacts/v2q_m8_03895c4299e6337d/cli/sql_stderr_attempt_1.txt ADDED
File without changes
Query/sql/v2/runs/v2_cli_20260502_081223_f/m8/artifacts/v2q_m8_03895c4299e6337d/cli/sql_stderr_attempt_2.txt ADDED
File without changes
Query/sql/v2/runs/v2_cli_20260502_081223_f/m8/artifacts/v2q_m8_06bd583986a0d664/cli/sql_attempt_1.metadata.json ADDED
@@ -0,0 +1,43 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "attempt": 1,
3
+ "phase": "sql_generation",
4
+ "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
5
+ "started_at": "2026-05-19T16:12:42.606607+00:00",
6
+ "ended_at": "2026-05-19T16:12:45.555488+00:00",
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+ "elapsed_ms": 2948.84,
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+ "prompt_metrics": {
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+ "bytes_utf8": 9347,
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+ "lines": 302,
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+ "estimated_tokens": null
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+ },
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+ "stdout_metrics": {
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+ "chars": 281,
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+ "stderr_metrics": {
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+ },
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+ "lines": 4,
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+ "estimated_tokens": null
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+ },
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+ "usage": {}
36
+ },
37
+ "status": "failed",
38
+ "error": "AI CLI command failed with exit code 1: ",
39
+ "prompt_path": "cli/sql_prompt_attempt_1.txt",
40
+ "response_path": "cli/sql_response_attempt_1.txt",
41
+ "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
42
+ "stderr_path": "cli/sql_stderr_attempt_1.txt"
43
+ }
Query/sql/v2/runs/v2_cli_20260502_081223_f/m8/artifacts/v2q_m8_06bd583986a0d664/cli/sql_attempt_2.metadata.json ADDED
@@ -0,0 +1,43 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "attempt": 2,
3
+ "phase": "sql_generation",
4
+ "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
5
+ "started_at": "2026-05-19T16:12:46.557803+00:00",
6
+ "ended_at": "2026-05-19T16:12:50.216294+00:00",
7
+ "elapsed_ms": 3658.44,
8
+ "returncode": 1,
9
+ "prompt_metrics": {
10
+ "chars": 9347,
11
+ "bytes_utf8": 9347,
12
+ "lines": 302,
13
+ "estimated_tokens": null
14
+ },
15
+ "stdout_metrics": {
16
+ "chars": 281,
17
+ "bytes_utf8": 281,
18
+ "lines": 4,
19
+ "estimated_tokens": null
20
+ },
21
+ "stderr_metrics": {
22
+ "chars": 0,
23
+ "bytes_utf8": 0,
24
+ "lines": 0,
25
+ "estimated_tokens": null
26
+ },
27
+ "parsed_output": {
28
+ "format": "jsonl_events",
29
+ "text_metrics": {
30
+ "chars": 280,
31
+ "bytes_utf8": 280,
32
+ "lines": 4,
33
+ "estimated_tokens": null
34
+ },
35
+ "usage": {}
36
+ },
37
+ "status": "failed",
38
+ "error": "AI CLI command failed with exit code 1: ",
39
+ "prompt_path": "cli/sql_prompt_attempt_2.txt",
40
+ "response_path": "cli/sql_response_attempt_2.txt",
41
+ "raw_response_path": "cli/sql_response_attempt_2.raw.txt",
42
+ "stderr_path": "cli/sql_stderr_attempt_2.txt"
43
+ }
Query/sql/v2/runs/v2_cli_20260502_081223_f/m8/artifacts/v2q_m8_06bd583986a0d664/cli/sql_prompt_attempt_1.txt ADDED
@@ -0,0 +1,302 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ You are generating one SQLite SELECT query for a single-table SQL QA task.
2
+ Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
3
+ Rules:
4
+ - Use only the provided table and columns.
5
+ - Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
6
+ - Prefer the planned template and bound roles when provided.
7
+ - Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
8
+ - Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
9
+ - Quote identifiers with double quotes.
10
+ - Return no markdown and no extra prose.
11
+
12
+ Dataset context:
13
+ Dataset context for SQL QA:
14
+ - dataset_id: m8
15
+ - dataset_name: Bank Marketing
16
+ - table_name: m8
17
+ - table_layout: single-table dataset (do not assume joins).
18
+ - row_semantics: One row is one tabular observation with 16 feature columns and target `y`.
19
+ - task_type: classification
20
+ - target_column: y
21
+ - main_row_count: 45211
22
+ - important_fields:
23
+ - age: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for age.
24
+ - job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for job.
25
+ - marital: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for marital.
26
+ - education: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for education.
27
+ - default: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for default.
28
+ - balance: role=feature, type=numeric. tags=['condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for balance.
29
+ - housing: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for housing.
30
+ - loan: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for loan.
31
+ - contact: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for contact.
32
+ - day: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for day.
33
+ - month: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for month.
34
+ - duration: role=feature, type=numeric. tags=['condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for duration.
35
+ - campaign: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for campaign.
36
+ - pdays: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for pdays.
37
+ - previous: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for previous.
38
+ - poutcome: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for poutcome.
39
+ - y: role=target, type=binary_target. tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for y.
40
+ - useful_field_combinations: [['age', 'job', 'y'], ['age', 'age', 'y']]
41
+ - fields_requiring_caution: ['y', 'balance', 'duration', 'pdays']
42
+ - source_url: https://archive.ics.uci.edu/dataset/222/bank+marketing
43
+
44
+ SQLite schema snapshot:
45
+ {
46
+ "table_name": "m8",
47
+ "quoted_table_name": "\"m8\"",
48
+ "row_count": 45211,
49
+ "columns": [
50
+ {
51
+ "name": "age",
52
+ "type": "TEXT",
53
+ "notnull": false,
54
+ "pk": false
55
+ },
56
+ {
57
+ "name": "job",
58
+ "type": "TEXT",
59
+ "notnull": false,
60
+ "pk": false
61
+ },
62
+ {
63
+ "name": "marital",
64
+ "type": "TEXT",
65
+ "notnull": false,
66
+ "pk": false
67
+ },
68
+ {
69
+ "name": "education",
70
+ "type": "TEXT",
71
+ "notnull": false,
72
+ "pk": false
73
+ },
74
+ {
75
+ "name": "default",
76
+ "type": "TEXT",
77
+ "notnull": false,
78
+ "pk": false
79
+ },
80
+ {
81
+ "name": "balance",
82
+ "type": "TEXT",
83
+ "notnull": false,
84
+ "pk": false
85
+ },
86
+ {
87
+ "name": "housing",
88
+ "type": "TEXT",
89
+ "notnull": false,
90
+ "pk": false
91
+ },
92
+ {
93
+ "name": "loan",
94
+ "type": "TEXT",
95
+ "notnull": false,
96
+ "pk": false
97
+ },
98
+ {
99
+ "name": "contact",
100
+ "type": "TEXT",
101
+ "notnull": false,
102
+ "pk": false
103
+ },
104
+ {
105
+ "name": "day",
106
+ "type": "TEXT",
107
+ "notnull": false,
108
+ "pk": false
109
+ },
110
+ {
111
+ "name": "month",
112
+ "type": "TEXT",
113
+ "notnull": false,
114
+ "pk": false
115
+ },
116
+ {
117
+ "name": "duration",
118
+ "type": "TEXT",
119
+ "notnull": false,
120
+ "pk": false
121
+ },
122
+ {
123
+ "name": "campaign",
124
+ "type": "TEXT",
125
+ "notnull": false,
126
+ "pk": false
127
+ },
128
+ {
129
+ "name": "pdays",
130
+ "type": "TEXT",
131
+ "notnull": false,
132
+ "pk": false
133
+ },
134
+ {
135
+ "name": "previous",
136
+ "type": "TEXT",
137
+ "notnull": false,
138
+ "pk": false
139
+ },
140
+ {
141
+ "name": "poutcome",
142
+ "type": "TEXT",
143
+ "notnull": false,
144
+ "pk": false
145
+ },
146
+ {
147
+ "name": "y",
148
+ "type": "TEXT",
149
+ "notnull": false,
150
+ "pk": false
151
+ }
152
+ ],
153
+ "sample_rows": [
154
+ {
155
+ "age": "58",
156
+ "job": "management",
157
+ "marital": "married",
158
+ "education": "tertiary",
159
+ "default": "no",
160
+ "balance": "2143",
161
+ "housing": "yes",
162
+ "loan": "no",
163
+ "contact": "unknown",
164
+ "day": "5",
165
+ "month": "may",
166
+ "duration": "261",
167
+ "campaign": "1",
168
+ "pdays": "-1",
169
+ "previous": "0",
170
+ "poutcome": "unknown",
171
+ "y": "no"
172
+ },
173
+ {
174
+ "age": "44",
175
+ "job": "technician",
176
+ "marital": "single",
177
+ "education": "secondary",
178
+ "default": "no",
179
+ "balance": "29",
180
+ "housing": "yes",
181
+ "loan": "no",
182
+ "contact": "unknown",
183
+ "day": "5",
184
+ "month": "may",
185
+ "duration": "151",
186
+ "campaign": "1",
187
+ "pdays": "-1",
188
+ "previous": "0",
189
+ "poutcome": "unknown",
190
+ "y": "no"
191
+ },
192
+ {
193
+ "age": "33",
194
+ "job": "entrepreneur",
195
+ "marital": "married",
196
+ "education": "secondary",
197
+ "default": "no",
198
+ "balance": "2",
199
+ "housing": "yes",
200
+ "loan": "yes",
201
+ "contact": "unknown",
202
+ "day": "5",
203
+ "month": "may",
204
+ "duration": "76",
205
+ "campaign": "1",
206
+ "pdays": "-1",
207
+ "previous": "0",
208
+ "poutcome": "unknown",
209
+ "y": "no"
210
+ },
211
+ {
212
+ "age": "47",
213
+ "job": "blue-collar",
214
+ "marital": "married",
215
+ "education": "unknown",
216
+ "default": "no",
217
+ "balance": "1506",
218
+ "housing": "yes",
219
+ "loan": "no",
220
+ "contact": "unknown",
221
+ "day": "5",
222
+ "month": "may",
223
+ "duration": "92",
224
+ "campaign": "1",
225
+ "pdays": "-1",
226
+ "previous": "0",
227
+ "poutcome": "unknown",
228
+ "y": "no"
229
+ },
230
+ {
231
+ "age": "33",
232
+ "job": "unknown",
233
+ "marital": "single",
234
+ "education": "unknown",
235
+ "default": "no",
236
+ "balance": "1",
237
+ "housing": "no",
238
+ "loan": "no",
239
+ "contact": "unknown",
240
+ "day": "5",
241
+ "month": "may",
242
+ "duration": "198",
243
+ "campaign": "1",
244
+ "pdays": "-1",
245
+ "previous": "0",
246
+ "poutcome": "unknown",
247
+ "y": "no"
248
+ }
249
+ ]
250
+ }
251
+
252
+ Shortlisted templates:
253
+ [
254
+ {
255
+ "template_id": "tpl_tpcds_within_group_share",
256
+ "template_name": "Within-Group Share of Total",
257
+ "primary_family": "conditional_dependency_structure",
258
+ "portability": "partial",
259
+ "sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;",
260
+ "required_roles": [
261
+ "group_col",
262
+ "item_col",
263
+ "measure_col"
264
+ ]
265
+ }
266
+ ]
267
+
268
+ Problem instance:
269
+ {
270
+ "dataset_id": "m8",
271
+ "question": "Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role focused_target_view. Focus on group_col=age, measure_col=day.",
272
+ "planned_template_id": "tpl_tpcds_within_group_share",
273
+ "bindings": {
274
+ "group_col": "age",
275
+ "measure_col": "day",
276
+ "item_col": "duration",
277
+ "top_k": 15,
278
+ "top_n": 6,
279
+ "num_tiles": 10,
280
+ "percentile_value": 0.9,
281
+ "z_threshold": 2.0,
282
+ "fraction_threshold": 0.05,
283
+ "baseline_multiplier": 1.75,
284
+ "baseline_fraction": 0.1,
285
+ "min_group_size": 5,
286
+ "min_support": 4,
287
+ "measure_threshold": 20.0,
288
+ "time_grain": "month",
289
+ "lookback_rows": 3,
290
+ "current_period_start": "'2024-01-01'",
291
+ "current_period_end": "'2024-04-01'",
292
+ "previous_period_start": "'2023-10-01'",
293
+ "previous_period_end": "'2024-01-01'",
294
+ "drift_ratio_threshold": 0.8
295
+ },
296
+ "can_vary": [],
297
+ "must_fix": [],
298
+ "runtime_sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;"
299
+ }
300
+
301
+ Repair context:
302
+ {}
Query/sql/v2/runs/v2_cli_20260502_081223_f/m8/artifacts/v2q_m8_06bd583986a0d664/cli/sql_prompt_attempt_2.txt ADDED
@@ -0,0 +1,302 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ You are generating one SQLite SELECT query for a single-table SQL QA task.
2
+ Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
3
+ Rules:
4
+ - Use only the provided table and columns.
5
+ - Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
6
+ - Prefer the planned template and bound roles when provided.
7
+ - Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
8
+ - Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
9
+ - Quote identifiers with double quotes.
10
+ - Return no markdown and no extra prose.
11
+
12
+ Dataset context:
13
+ Dataset context for SQL QA:
14
+ - dataset_id: m8
15
+ - dataset_name: Bank Marketing
16
+ - table_name: m8
17
+ - table_layout: single-table dataset (do not assume joins).
18
+ - row_semantics: One row is one tabular observation with 16 feature columns and target `y`.
19
+ - task_type: classification
20
+ - target_column: y
21
+ - main_row_count: 45211
22
+ - important_fields:
23
+ - age: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for age.
24
+ - job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for job.
25
+ - marital: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for marital.
26
+ - education: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for education.
27
+ - default: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for default.
28
+ - balance: role=feature, type=numeric. tags=['condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for balance.
29
+ - housing: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for housing.
30
+ - loan: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for loan.
31
+ - contact: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for contact.
32
+ - day: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for day.
33
+ - month: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for month.
34
+ - duration: role=feature, type=numeric. tags=['condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for duration.
35
+ - campaign: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for campaign.
36
+ - pdays: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for pdays.
37
+ - previous: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for previous.
38
+ - poutcome: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for poutcome.
39
+ - y: role=target, type=binary_target. tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for y.
40
+ - useful_field_combinations: [['age', 'job', 'y'], ['age', 'age', 'y']]
41
+ - fields_requiring_caution: ['y', 'balance', 'duration', 'pdays']
42
+ - source_url: https://archive.ics.uci.edu/dataset/222/bank+marketing
43
+
44
+ SQLite schema snapshot:
45
+ {
46
+ "table_name": "m8",
47
+ "quoted_table_name": "\"m8\"",
48
+ "row_count": 45211,
49
+ "columns": [
50
+ {
51
+ "name": "age",
52
+ "type": "TEXT",
53
+ "notnull": false,
54
+ "pk": false
55
+ },
56
+ {
57
+ "name": "job",
58
+ "type": "TEXT",
59
+ "notnull": false,
60
+ "pk": false
61
+ },
62
+ {
63
+ "name": "marital",
64
+ "type": "TEXT",
65
+ "notnull": false,
66
+ "pk": false
67
+ },
68
+ {
69
+ "name": "education",
70
+ "type": "TEXT",
71
+ "notnull": false,
72
+ "pk": false
73
+ },
74
+ {
75
+ "name": "default",
76
+ "type": "TEXT",
77
+ "notnull": false,
78
+ "pk": false
79
+ },
80
+ {
81
+ "name": "balance",
82
+ "type": "TEXT",
83
+ "notnull": false,
84
+ "pk": false
85
+ },
86
+ {
87
+ "name": "housing",
88
+ "type": "TEXT",
89
+ "notnull": false,
90
+ "pk": false
91
+ },
92
+ {
93
+ "name": "loan",
94
+ "type": "TEXT",
95
+ "notnull": false,
96
+ "pk": false
97
+ },
98
+ {
99
+ "name": "contact",
100
+ "type": "TEXT",
101
+ "notnull": false,
102
+ "pk": false
103
+ },
104
+ {
105
+ "name": "day",
106
+ "type": "TEXT",
107
+ "notnull": false,
108
+ "pk": false
109
+ },
110
+ {
111
+ "name": "month",
112
+ "type": "TEXT",
113
+ "notnull": false,
114
+ "pk": false
115
+ },
116
+ {
117
+ "name": "duration",
118
+ "type": "TEXT",
119
+ "notnull": false,
120
+ "pk": false
121
+ },
122
+ {
123
+ "name": "campaign",
124
+ "type": "TEXT",
125
+ "notnull": false,
126
+ "pk": false
127
+ },
128
+ {
129
+ "name": "pdays",
130
+ "type": "TEXT",
131
+ "notnull": false,
132
+ "pk": false
133
+ },
134
+ {
135
+ "name": "previous",
136
+ "type": "TEXT",
137
+ "notnull": false,
138
+ "pk": false
139
+ },
140
+ {
141
+ "name": "poutcome",
142
+ "type": "TEXT",
143
+ "notnull": false,
144
+ "pk": false
145
+ },
146
+ {
147
+ "name": "y",
148
+ "type": "TEXT",
149
+ "notnull": false,
150
+ "pk": false
151
+ }
152
+ ],
153
+ "sample_rows": [
154
+ {
155
+ "age": "58",
156
+ "job": "management",
157
+ "marital": "married",
158
+ "education": "tertiary",
159
+ "default": "no",
160
+ "balance": "2143",
161
+ "housing": "yes",
162
+ "loan": "no",
163
+ "contact": "unknown",
164
+ "day": "5",
165
+ "month": "may",
166
+ "duration": "261",
167
+ "campaign": "1",
168
+ "pdays": "-1",
169
+ "previous": "0",
170
+ "poutcome": "unknown",
171
+ "y": "no"
172
+ },
173
+ {
174
+ "age": "44",
175
+ "job": "technician",
176
+ "marital": "single",
177
+ "education": "secondary",
178
+ "default": "no",
179
+ "balance": "29",
180
+ "housing": "yes",
181
+ "loan": "no",
182
+ "contact": "unknown",
183
+ "day": "5",
184
+ "month": "may",
185
+ "duration": "151",
186
+ "campaign": "1",
187
+ "pdays": "-1",
188
+ "previous": "0",
189
+ "poutcome": "unknown",
190
+ "y": "no"
191
+ },
192
+ {
193
+ "age": "33",
194
+ "job": "entrepreneur",
195
+ "marital": "married",
196
+ "education": "secondary",
197
+ "default": "no",
198
+ "balance": "2",
199
+ "housing": "yes",
200
+ "loan": "yes",
201
+ "contact": "unknown",
202
+ "day": "5",
203
+ "month": "may",
204
+ "duration": "76",
205
+ "campaign": "1",
206
+ "pdays": "-1",
207
+ "previous": "0",
208
+ "poutcome": "unknown",
209
+ "y": "no"
210
+ },
211
+ {
212
+ "age": "47",
213
+ "job": "blue-collar",
214
+ "marital": "married",
215
+ "education": "unknown",
216
+ "default": "no",
217
+ "balance": "1506",
218
+ "housing": "yes",
219
+ "loan": "no",
220
+ "contact": "unknown",
221
+ "day": "5",
222
+ "month": "may",
223
+ "duration": "92",
224
+ "campaign": "1",
225
+ "pdays": "-1",
226
+ "previous": "0",
227
+ "poutcome": "unknown",
228
+ "y": "no"
229
+ },
230
+ {
231
+ "age": "33",
232
+ "job": "unknown",
233
+ "marital": "single",
234
+ "education": "unknown",
235
+ "default": "no",
236
+ "balance": "1",
237
+ "housing": "no",
238
+ "loan": "no",
239
+ "contact": "unknown",
240
+ "day": "5",
241
+ "month": "may",
242
+ "duration": "198",
243
+ "campaign": "1",
244
+ "pdays": "-1",
245
+ "previous": "0",
246
+ "poutcome": "unknown",
247
+ "y": "no"
248
+ }
249
+ ]
250
+ }
251
+
252
+ Shortlisted templates:
253
+ [
254
+ {
255
+ "template_id": "tpl_tpcds_within_group_share",
256
+ "template_name": "Within-Group Share of Total",
257
+ "primary_family": "conditional_dependency_structure",
258
+ "portability": "partial",
259
+ "sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;",
260
+ "required_roles": [
261
+ "group_col",
262
+ "item_col",
263
+ "measure_col"
264
+ ]
265
+ }
266
+ ]
267
+
268
+ Problem instance:
269
+ {
270
+ "dataset_id": "m8",
271
+ "question": "Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role focused_target_view. Focus on group_col=age, measure_col=day.",
272
+ "planned_template_id": "tpl_tpcds_within_group_share",
273
+ "bindings": {
274
+ "group_col": "age",
275
+ "measure_col": "day",
276
+ "item_col": "duration",
277
+ "top_k": 15,
278
+ "top_n": 6,
279
+ "num_tiles": 10,
280
+ "percentile_value": 0.9,
281
+ "z_threshold": 2.0,
282
+ "fraction_threshold": 0.05,
283
+ "baseline_multiplier": 1.75,
284
+ "baseline_fraction": 0.1,
285
+ "min_group_size": 5,
286
+ "min_support": 4,
287
+ "measure_threshold": 20.0,
288
+ "time_grain": "month",
289
+ "lookback_rows": 3,
290
+ "current_period_start": "'2024-01-01'",
291
+ "current_period_end": "'2024-04-01'",
292
+ "previous_period_start": "'2023-10-01'",
293
+ "previous_period_end": "'2024-01-01'",
294
+ "drift_ratio_threshold": 0.8
295
+ },
296
+ "can_vary": [],
297
+ "must_fix": [],
298
+ "runtime_sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;"
299
+ }
300
+
301
+ Repair context:
302
+ {}
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+ {"type":"turn.started"}
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+ {"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
Query/sql/v2/runs/v2_cli_20260502_081223_f/m8/artifacts/v2q_m8_06bd583986a0d664/cli/sql_response_attempt_2.txt ADDED
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+ {"type":"turn.started"}
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+ {"type":"error","message":"Quota exceeded. Check your plan and billing details."}
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+ {"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
Query/sql/v2/runs/v2_cli_20260502_081223_f/m8/artifacts/v2q_m8_06bd583986a0d664/cli/sql_stderr_attempt_1.txt ADDED
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File without changes
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+ {
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+ "attempt": 1,
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+ "phase": "sql_generation",
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+ "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
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+ "started_at": "2026-05-19T16:12:20.520459+00:00",
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+ "elapsed_ms": 3232.97,
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+ "prompt_metrics": {
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+ },
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+ },
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+ "status": "failed",
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+ "error": "AI CLI command failed with exit code 1: ",
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+ "prompt_path": "cli/sql_prompt_attempt_1.txt",
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+ "response_path": "cli/sql_response_attempt_1.txt",
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+ "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
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+ "stderr_path": "cli/sql_stderr_attempt_1.txt"
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+ }
Query/sql/v2/runs/v2_cli_20260502_081223_f/m8/artifacts/v2q_m8_0c4031cf0e95f112/cli/sql_attempt_2.metadata.json ADDED
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+ {
2
+ "attempt": 2,
3
+ "phase": "sql_generation",
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+ "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
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+ "started_at": "2026-05-19T16:12:24.756412+00:00",
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+ },
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+ "status": "failed",
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+ "error": "AI CLI command failed with exit code 1: ",
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+ "prompt_path": "cli/sql_prompt_attempt_2.txt",
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+ "response_path": "cli/sql_response_attempt_2.txt",
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+ "raw_response_path": "cli/sql_response_attempt_2.raw.txt",
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+ "stderr_path": "cli/sql_stderr_attempt_2.txt"
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+ }
Query/sql/v2/runs/v2_cli_20260502_081223_f/m8/artifacts/v2q_m8_0c4031cf0e95f112/cli/sql_prompt_attempt_1.txt ADDED
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1
+ You are generating one SQLite SELECT query for a single-table SQL QA task.
2
+ Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
3
+ Rules:
4
+ - Use only the provided table and columns.
5
+ - Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
6
+ - Prefer the planned template and bound roles when provided.
7
+ - Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
8
+ - Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
9
+ - Quote identifiers with double quotes.
10
+ - Return no markdown and no extra prose.
11
+
12
+ Dataset context:
13
+ Dataset context for SQL QA:
14
+ - dataset_id: m8
15
+ - dataset_name: Bank Marketing
16
+ - table_name: m8
17
+ - table_layout: single-table dataset (do not assume joins).
18
+ - row_semantics: One row is one tabular observation with 16 feature columns and target `y`.
19
+ - task_type: classification
20
+ - target_column: y
21
+ - main_row_count: 45211
22
+ - important_fields:
23
+ - age: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for age.
24
+ - job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for job.
25
+ - marital: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for marital.
26
+ - education: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for education.
27
+ - default: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for default.
28
+ - balance: role=feature, type=numeric. tags=['condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for balance.
29
+ - housing: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for housing.
30
+ - loan: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for loan.
31
+ - contact: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for contact.
32
+ - day: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for day.
33
+ - month: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for month.
34
+ - duration: role=feature, type=numeric. tags=['condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for duration.
35
+ - campaign: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for campaign.
36
+ - pdays: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for pdays.
37
+ - previous: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for previous.
38
+ - poutcome: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for poutcome.
39
+ - y: role=target, type=binary_target. tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for y.
40
+ - useful_field_combinations: [['age', 'job', 'y'], ['age', 'age', 'y']]
41
+ - fields_requiring_caution: ['y', 'balance', 'duration', 'pdays']
42
+ - source_url: https://archive.ics.uci.edu/dataset/222/bank+marketing
43
+
44
+ SQLite schema snapshot:
45
+ {
46
+ "table_name": "m8",
47
+ "quoted_table_name": "\"m8\"",
48
+ "row_count": 45211,
49
+ "columns": [
50
+ {
51
+ "name": "age",
52
+ "type": "TEXT",
53
+ "notnull": false,
54
+ "pk": false
55
+ },
56
+ {
57
+ "name": "job",
58
+ "type": "TEXT",
59
+ "notnull": false,
60
+ "pk": false
61
+ },
62
+ {
63
+ "name": "marital",
64
+ "type": "TEXT",
65
+ "notnull": false,
66
+ "pk": false
67
+ },
68
+ {
69
+ "name": "education",
70
+ "type": "TEXT",
71
+ "notnull": false,
72
+ "pk": false
73
+ },
74
+ {
75
+ "name": "default",
76
+ "type": "TEXT",
77
+ "notnull": false,
78
+ "pk": false
79
+ },
80
+ {
81
+ "name": "balance",
82
+ "type": "TEXT",
83
+ "notnull": false,
84
+ "pk": false
85
+ },
86
+ {
87
+ "name": "housing",
88
+ "type": "TEXT",
89
+ "notnull": false,
90
+ "pk": false
91
+ },
92
+ {
93
+ "name": "loan",
94
+ "type": "TEXT",
95
+ "notnull": false,
96
+ "pk": false
97
+ },
98
+ {
99
+ "name": "contact",
100
+ "type": "TEXT",
101
+ "notnull": false,
102
+ "pk": false
103
+ },
104
+ {
105
+ "name": "day",
106
+ "type": "TEXT",
107
+ "notnull": false,
108
+ "pk": false
109
+ },
110
+ {
111
+ "name": "month",
112
+ "type": "TEXT",
113
+ "notnull": false,
114
+ "pk": false
115
+ },
116
+ {
117
+ "name": "duration",
118
+ "type": "TEXT",
119
+ "notnull": false,
120
+ "pk": false
121
+ },
122
+ {
123
+ "name": "campaign",
124
+ "type": "TEXT",
125
+ "notnull": false,
126
+ "pk": false
127
+ },
128
+ {
129
+ "name": "pdays",
130
+ "type": "TEXT",
131
+ "notnull": false,
132
+ "pk": false
133
+ },
134
+ {
135
+ "name": "previous",
136
+ "type": "TEXT",
137
+ "notnull": false,
138
+ "pk": false
139
+ },
140
+ {
141
+ "name": "poutcome",
142
+ "type": "TEXT",
143
+ "notnull": false,
144
+ "pk": false
145
+ },
146
+ {
147
+ "name": "y",
148
+ "type": "TEXT",
149
+ "notnull": false,
150
+ "pk": false
151
+ }
152
+ ],
153
+ "sample_rows": [
154
+ {
155
+ "age": "58",
156
+ "job": "management",
157
+ "marital": "married",
158
+ "education": "tertiary",
159
+ "default": "no",
160
+ "balance": "2143",
161
+ "housing": "yes",
162
+ "loan": "no",
163
+ "contact": "unknown",
164
+ "day": "5",
165
+ "month": "may",
166
+ "duration": "261",
167
+ "campaign": "1",
168
+ "pdays": "-1",
169
+ "previous": "0",
170
+ "poutcome": "unknown",
171
+ "y": "no"
172
+ },
173
+ {
174
+ "age": "44",
175
+ "job": "technician",
176
+ "marital": "single",
177
+ "education": "secondary",
178
+ "default": "no",
179
+ "balance": "29",
180
+ "housing": "yes",
181
+ "loan": "no",
182
+ "contact": "unknown",
183
+ "day": "5",
184
+ "month": "may",
185
+ "duration": "151",
186
+ "campaign": "1",
187
+ "pdays": "-1",
188
+ "previous": "0",
189
+ "poutcome": "unknown",
190
+ "y": "no"
191
+ },
192
+ {
193
+ "age": "33",
194
+ "job": "entrepreneur",
195
+ "marital": "married",
196
+ "education": "secondary",
197
+ "default": "no",
198
+ "balance": "2",
199
+ "housing": "yes",
200
+ "loan": "yes",
201
+ "contact": "unknown",
202
+ "day": "5",
203
+ "month": "may",
204
+ "duration": "76",
205
+ "campaign": "1",
206
+ "pdays": "-1",
207
+ "previous": "0",
208
+ "poutcome": "unknown",
209
+ "y": "no"
210
+ },
211
+ {
212
+ "age": "47",
213
+ "job": "blue-collar",
214
+ "marital": "married",
215
+ "education": "unknown",
216
+ "default": "no",
217
+ "balance": "1506",
218
+ "housing": "yes",
219
+ "loan": "no",
220
+ "contact": "unknown",
221
+ "day": "5",
222
+ "month": "may",
223
+ "duration": "92",
224
+ "campaign": "1",
225
+ "pdays": "-1",
226
+ "previous": "0",
227
+ "poutcome": "unknown",
228
+ "y": "no"
229
+ },
230
+ {
231
+ "age": "33",
232
+ "job": "unknown",
233
+ "marital": "single",
234
+ "education": "unknown",
235
+ "default": "no",
236
+ "balance": "1",
237
+ "housing": "no",
238
+ "loan": "no",
239
+ "contact": "unknown",
240
+ "day": "5",
241
+ "month": "may",
242
+ "duration": "198",
243
+ "campaign": "1",
244
+ "pdays": "-1",
245
+ "previous": "0",
246
+ "poutcome": "unknown",
247
+ "y": "no"
248
+ }
249
+ ]
250
+ }
251
+
252
+ Shortlisted templates:
253
+ [
254
+ {
255
+ "template_id": "tpl_tpcds_within_group_share",
256
+ "template_name": "Within-Group Share of Total",
257
+ "primary_family": "conditional_dependency_structure",
258
+ "portability": "partial",
259
+ "sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;",
260
+ "required_roles": [
261
+ "group_col",
262
+ "item_col",
263
+ "measure_col"
264
+ ]
265
+ }
266
+ ]
267
+
268
+ Problem instance:
269
+ {
270
+ "dataset_id": "m8",
271
+ "question": "Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role focused_target_view. Focus on group_col=y, measure_col=balance.",
272
+ "planned_template_id": "tpl_tpcds_within_group_share",
273
+ "bindings": {
274
+ "group_col": "y",
275
+ "measure_col": "balance",
276
+ "item_col": "day",
277
+ "top_k": 14,
278
+ "top_n": 4,
279
+ "num_tiles": 10,
280
+ "percentile_value": 0.9,
281
+ "z_threshold": 2.0,
282
+ "fraction_threshold": 0.1,
283
+ "baseline_multiplier": 1.5,
284
+ "baseline_fraction": 0.1,
285
+ "min_group_size": 5,
286
+ "min_support": 5,
287
+ "measure_threshold": 1428.0,
288
+ "time_grain": "month",
289
+ "lookback_rows": 3,
290
+ "current_period_start": "'2024-01-01'",
291
+ "current_period_end": "'2024-04-01'",
292
+ "previous_period_start": "'2023-10-01'",
293
+ "previous_period_end": "'2024-01-01'",
294
+ "drift_ratio_threshold": 0.8
295
+ },
296
+ "can_vary": [],
297
+ "must_fix": [],
298
+ "runtime_sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;"
299
+ }
300
+
301
+ Repair context:
302
+ {}
Query/sql/v2/runs/v2_cli_20260502_081223_f/m8/artifacts/v2q_m8_0c4031cf0e95f112/cli/sql_prompt_attempt_2.txt ADDED
@@ -0,0 +1,302 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ You are generating one SQLite SELECT query for a single-table SQL QA task.
2
+ Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
3
+ Rules:
4
+ - Use only the provided table and columns.
5
+ - Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
6
+ - Prefer the planned template and bound roles when provided.
7
+ - Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
8
+ - Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
9
+ - Quote identifiers with double quotes.
10
+ - Return no markdown and no extra prose.
11
+
12
+ Dataset context:
13
+ Dataset context for SQL QA:
14
+ - dataset_id: m8
15
+ - dataset_name: Bank Marketing
16
+ - table_name: m8
17
+ - table_layout: single-table dataset (do not assume joins).
18
+ - row_semantics: One row is one tabular observation with 16 feature columns and target `y`.
19
+ - task_type: classification
20
+ - target_column: y
21
+ - main_row_count: 45211
22
+ - important_fields:
23
+ - age: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for age.
24
+ - job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for job.
25
+ - marital: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for marital.
26
+ - education: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for education.
27
+ - default: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for default.
28
+ - balance: role=feature, type=numeric. tags=['condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for balance.
29
+ - housing: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for housing.
30
+ - loan: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for loan.
31
+ - contact: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for contact.
32
+ - day: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for day.
33
+ - month: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for month.
34
+ - duration: role=feature, type=numeric. tags=['condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for duration.
35
+ - campaign: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for campaign.
36
+ - pdays: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for pdays.
37
+ - previous: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for previous.
38
+ - poutcome: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for poutcome.
39
+ - y: role=target, type=binary_target. tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for y.
40
+ - useful_field_combinations: [['age', 'job', 'y'], ['age', 'age', 'y']]
41
+ - fields_requiring_caution: ['y', 'balance', 'duration', 'pdays']
42
+ - source_url: https://archive.ics.uci.edu/dataset/222/bank+marketing
43
+
44
+ SQLite schema snapshot:
45
+ {
46
+ "table_name": "m8",
47
+ "quoted_table_name": "\"m8\"",
48
+ "row_count": 45211,
49
+ "columns": [
50
+ {
51
+ "name": "age",
52
+ "type": "TEXT",
53
+ "notnull": false,
54
+ "pk": false
55
+ },
56
+ {
57
+ "name": "job",
58
+ "type": "TEXT",
59
+ "notnull": false,
60
+ "pk": false
61
+ },
62
+ {
63
+ "name": "marital",
64
+ "type": "TEXT",
65
+ "notnull": false,
66
+ "pk": false
67
+ },
68
+ {
69
+ "name": "education",
70
+ "type": "TEXT",
71
+ "notnull": false,
72
+ "pk": false
73
+ },
74
+ {
75
+ "name": "default",
76
+ "type": "TEXT",
77
+ "notnull": false,
78
+ "pk": false
79
+ },
80
+ {
81
+ "name": "balance",
82
+ "type": "TEXT",
83
+ "notnull": false,
84
+ "pk": false
85
+ },
86
+ {
87
+ "name": "housing",
88
+ "type": "TEXT",
89
+ "notnull": false,
90
+ "pk": false
91
+ },
92
+ {
93
+ "name": "loan",
94
+ "type": "TEXT",
95
+ "notnull": false,
96
+ "pk": false
97
+ },
98
+ {
99
+ "name": "contact",
100
+ "type": "TEXT",
101
+ "notnull": false,
102
+ "pk": false
103
+ },
104
+ {
105
+ "name": "day",
106
+ "type": "TEXT",
107
+ "notnull": false,
108
+ "pk": false
109
+ },
110
+ {
111
+ "name": "month",
112
+ "type": "TEXT",
113
+ "notnull": false,
114
+ "pk": false
115
+ },
116
+ {
117
+ "name": "duration",
118
+ "type": "TEXT",
119
+ "notnull": false,
120
+ "pk": false
121
+ },
122
+ {
123
+ "name": "campaign",
124
+ "type": "TEXT",
125
+ "notnull": false,
126
+ "pk": false
127
+ },
128
+ {
129
+ "name": "pdays",
130
+ "type": "TEXT",
131
+ "notnull": false,
132
+ "pk": false
133
+ },
134
+ {
135
+ "name": "previous",
136
+ "type": "TEXT",
137
+ "notnull": false,
138
+ "pk": false
139
+ },
140
+ {
141
+ "name": "poutcome",
142
+ "type": "TEXT",
143
+ "notnull": false,
144
+ "pk": false
145
+ },
146
+ {
147
+ "name": "y",
148
+ "type": "TEXT",
149
+ "notnull": false,
150
+ "pk": false
151
+ }
152
+ ],
153
+ "sample_rows": [
154
+ {
155
+ "age": "58",
156
+ "job": "management",
157
+ "marital": "married",
158
+ "education": "tertiary",
159
+ "default": "no",
160
+ "balance": "2143",
161
+ "housing": "yes",
162
+ "loan": "no",
163
+ "contact": "unknown",
164
+ "day": "5",
165
+ "month": "may",
166
+ "duration": "261",
167
+ "campaign": "1",
168
+ "pdays": "-1",
169
+ "previous": "0",
170
+ "poutcome": "unknown",
171
+ "y": "no"
172
+ },
173
+ {
174
+ "age": "44",
175
+ "job": "technician",
176
+ "marital": "single",
177
+ "education": "secondary",
178
+ "default": "no",
179
+ "balance": "29",
180
+ "housing": "yes",
181
+ "loan": "no",
182
+ "contact": "unknown",
183
+ "day": "5",
184
+ "month": "may",
185
+ "duration": "151",
186
+ "campaign": "1",
187
+ "pdays": "-1",
188
+ "previous": "0",
189
+ "poutcome": "unknown",
190
+ "y": "no"
191
+ },
192
+ {
193
+ "age": "33",
194
+ "job": "entrepreneur",
195
+ "marital": "married",
196
+ "education": "secondary",
197
+ "default": "no",
198
+ "balance": "2",
199
+ "housing": "yes",
200
+ "loan": "yes",
201
+ "contact": "unknown",
202
+ "day": "5",
203
+ "month": "may",
204
+ "duration": "76",
205
+ "campaign": "1",
206
+ "pdays": "-1",
207
+ "previous": "0",
208
+ "poutcome": "unknown",
209
+ "y": "no"
210
+ },
211
+ {
212
+ "age": "47",
213
+ "job": "blue-collar",
214
+ "marital": "married",
215
+ "education": "unknown",
216
+ "default": "no",
217
+ "balance": "1506",
218
+ "housing": "yes",
219
+ "loan": "no",
220
+ "contact": "unknown",
221
+ "day": "5",
222
+ "month": "may",
223
+ "duration": "92",
224
+ "campaign": "1",
225
+ "pdays": "-1",
226
+ "previous": "0",
227
+ "poutcome": "unknown",
228
+ "y": "no"
229
+ },
230
+ {
231
+ "age": "33",
232
+ "job": "unknown",
233
+ "marital": "single",
234
+ "education": "unknown",
235
+ "default": "no",
236
+ "balance": "1",
237
+ "housing": "no",
238
+ "loan": "no",
239
+ "contact": "unknown",
240
+ "day": "5",
241
+ "month": "may",
242
+ "duration": "198",
243
+ "campaign": "1",
244
+ "pdays": "-1",
245
+ "previous": "0",
246
+ "poutcome": "unknown",
247
+ "y": "no"
248
+ }
249
+ ]
250
+ }
251
+
252
+ Shortlisted templates:
253
+ [
254
+ {
255
+ "template_id": "tpl_tpcds_within_group_share",
256
+ "template_name": "Within-Group Share of Total",
257
+ "primary_family": "conditional_dependency_structure",
258
+ "portability": "partial",
259
+ "sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;",
260
+ "required_roles": [
261
+ "group_col",
262
+ "item_col",
263
+ "measure_col"
264
+ ]
265
+ }
266
+ ]
267
+
268
+ Problem instance:
269
+ {
270
+ "dataset_id": "m8",
271
+ "question": "Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role focused_target_view. Focus on group_col=y, measure_col=balance.",
272
+ "planned_template_id": "tpl_tpcds_within_group_share",
273
+ "bindings": {
274
+ "group_col": "y",
275
+ "measure_col": "balance",
276
+ "item_col": "day",
277
+ "top_k": 14,
278
+ "top_n": 4,
279
+ "num_tiles": 10,
280
+ "percentile_value": 0.9,
281
+ "z_threshold": 2.0,
282
+ "fraction_threshold": 0.1,
283
+ "baseline_multiplier": 1.5,
284
+ "baseline_fraction": 0.1,
285
+ "min_group_size": 5,
286
+ "min_support": 5,
287
+ "measure_threshold": 1428.0,
288
+ "time_grain": "month",
289
+ "lookback_rows": 3,
290
+ "current_period_start": "'2024-01-01'",
291
+ "current_period_end": "'2024-04-01'",
292
+ "previous_period_start": "'2023-10-01'",
293
+ "previous_period_end": "'2024-01-01'",
294
+ "drift_ratio_threshold": 0.8
295
+ },
296
+ "can_vary": [],
297
+ "must_fix": [],
298
+ "runtime_sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;"
299
+ }
300
+
301
+ Repair context:
302
+ {}
Query/sql/v2/runs/v2_cli_20260502_081223_f/m8/artifacts/v2q_m8_0c4031cf0e95f112/cli/sql_response_attempt_1.raw.txt ADDED
@@ -0,0 +1,4 @@
 
 
 
 
 
1
+ {"type":"thread.started","thread_id":"019e4102-75a9-72d1-9776-6ec7b9785575"}
2
+ {"type":"turn.started"}
3
+ {"type":"error","message":"Quota exceeded. Check your plan and billing details."}
4
+ {"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
Query/sql/v2/runs/v2_cli_20260502_081223_f/m8/artifacts/v2q_m8_0c4031cf0e95f112/cli/sql_response_attempt_1.txt ADDED
@@ -0,0 +1,4 @@
 
 
 
 
 
1
+ {"type":"thread.started","thread_id":"019e4102-75a9-72d1-9776-6ec7b9785575"}
2
+ {"type":"turn.started"}
3
+ {"type":"error","message":"Quota exceeded. Check your plan and billing details."}
4
+ {"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
Query/sql/v2/runs/v2_cli_20260502_081223_f/m8/artifacts/v2q_m8_0c4031cf0e95f112/cli/sql_response_attempt_2.raw.txt ADDED
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2
+ {"type":"turn.started"}
3
+ {"type":"error","message":"Quota exceeded. Check your plan and billing details."}
4
+ {"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
Query/sql/v2/runs/v2_cli_20260502_081223_f/m8/artifacts/v2q_m8_0c4031cf0e95f112/cli/sql_response_attempt_2.txt ADDED
@@ -0,0 +1,4 @@
 
 
 
 
 
1
+ {"type":"thread.started","thread_id":"019e4102-8637-7111-9252-109afd1e35e9"}
2
+ {"type":"turn.started"}
3
+ {"type":"error","message":"Quota exceeded. Check your plan and billing details."}
4
+ {"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
Query/sql/v2/runs/v2_cli_20260502_081223_f/m8/artifacts/v2q_m8_0c4031cf0e95f112/cli/sql_stderr_attempt_1.txt ADDED
File without changes
Query/sql/v2/runs/v2_cli_20260502_081223_f/m8/artifacts/v2q_m8_0c4031cf0e95f112/cli/sql_stderr_attempt_2.txt ADDED
File without changes
Query/sql/v2/runs/v2_cli_20260502_081223_f/m8/artifacts/v2q_m8_0d9ded01382f8312/cli/sql_attempt_1.metadata.json ADDED
@@ -0,0 +1,43 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "attempt": 1,
3
+ "phase": "sql_generation",
4
+ "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
5
+ "started_at": "2026-05-19T16:19:12.143701+00:00",
6
+ "ended_at": "2026-05-19T16:19:18.292765+00:00",
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+ "elapsed_ms": 6149.03,
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+ "returncode": 1,
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+ "prompt_metrics": {
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+ "bytes_utf8": 9516,
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+ "stdout_metrics": {
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+ "stderr_metrics": {
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+ },
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+ "parsed_output": {
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+ },
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+ "status": "failed",
38
+ "error": "AI CLI command failed with exit code 1: ",
39
+ "prompt_path": "cli/sql_prompt_attempt_1.txt",
40
+ "response_path": "cli/sql_response_attempt_1.txt",
41
+ "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
42
+ "stderr_path": "cli/sql_stderr_attempt_1.txt"
43
+ }
Query/sql/v2/runs/v2_cli_20260502_081223_f/m8/artifacts/v2q_m8_0d9ded01382f8312/cli/sql_attempt_2.metadata.json ADDED
@@ -0,0 +1,43 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "attempt": 2,
3
+ "phase": "sql_generation",
4
+ "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
5
+ "started_at": "2026-05-19T16:19:19.294676+00:00",
6
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7
+ "elapsed_ms": 5001.6,
8
+ "returncode": 1,
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+ "prompt_metrics": {
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+ "lines": 300,
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+ "stdout_metrics": {
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+ "stderr_metrics": {
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24
+ "lines": 0,
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+ "estimated_tokens": null
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+ },
27
+ "parsed_output": {
28
+ "format": "jsonl_events",
29
+ "text_metrics": {
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+ "chars": 280,
31
+ "bytes_utf8": 280,
32
+ "lines": 4,
33
+ "estimated_tokens": null
34
+ },
35
+ "usage": {}
36
+ },
37
+ "status": "failed",
38
+ "error": "AI CLI command failed with exit code 1: ",
39
+ "prompt_path": "cli/sql_prompt_attempt_2.txt",
40
+ "response_path": "cli/sql_response_attempt_2.txt",
41
+ "raw_response_path": "cli/sql_response_attempt_2.raw.txt",
42
+ "stderr_path": "cli/sql_stderr_attempt_2.txt"
43
+ }
Query/sql/v2/runs/v2_cli_20260502_081223_f/m8/artifacts/v2q_m8_0d9ded01382f8312/cli/sql_prompt_attempt_1.txt ADDED
@@ -0,0 +1,300 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ You are generating one SQLite SELECT query for a single-table SQL QA task.
2
+ Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
3
+ Rules:
4
+ - Use only the provided table and columns.
5
+ - Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
6
+ - Prefer the planned template and bound roles when provided.
7
+ - Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
8
+ - Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
9
+ - Quote identifiers with double quotes.
10
+ - Return no markdown and no extra prose.
11
+
12
+ Dataset context:
13
+ Dataset context for SQL QA:
14
+ - dataset_id: m8
15
+ - dataset_name: Bank Marketing
16
+ - table_name: m8
17
+ - table_layout: single-table dataset (do not assume joins).
18
+ - row_semantics: One row is one tabular observation with 16 feature columns and target `y`.
19
+ - task_type: classification
20
+ - target_column: y
21
+ - main_row_count: 45211
22
+ - important_fields:
23
+ - age: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for age.
24
+ - job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for job.
25
+ - marital: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for marital.
26
+ - education: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for education.
27
+ - default: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for default.
28
+ - balance: role=feature, type=numeric. tags=['condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for balance.
29
+ - housing: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for housing.
30
+ - loan: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for loan.
31
+ - contact: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for contact.
32
+ - day: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for day.
33
+ - month: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for month.
34
+ - duration: role=feature, type=numeric. tags=['condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for duration.
35
+ - campaign: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for campaign.
36
+ - pdays: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for pdays.
37
+ - previous: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for previous.
38
+ - poutcome: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for poutcome.
39
+ - y: role=target, type=binary_target. tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for y.
40
+ - useful_field_combinations: [['age', 'job', 'y'], ['age', 'age', 'y']]
41
+ - fields_requiring_caution: ['y', 'balance', 'duration', 'pdays']
42
+ - source_url: https://archive.ics.uci.edu/dataset/222/bank+marketing
43
+
44
+ SQLite schema snapshot:
45
+ {
46
+ "table_name": "m8",
47
+ "quoted_table_name": "\"m8\"",
48
+ "row_count": 45211,
49
+ "columns": [
50
+ {
51
+ "name": "age",
52
+ "type": "TEXT",
53
+ "notnull": false,
54
+ "pk": false
55
+ },
56
+ {
57
+ "name": "job",
58
+ "type": "TEXT",
59
+ "notnull": false,
60
+ "pk": false
61
+ },
62
+ {
63
+ "name": "marital",
64
+ "type": "TEXT",
65
+ "notnull": false,
66
+ "pk": false
67
+ },
68
+ {
69
+ "name": "education",
70
+ "type": "TEXT",
71
+ "notnull": false,
72
+ "pk": false
73
+ },
74
+ {
75
+ "name": "default",
76
+ "type": "TEXT",
77
+ "notnull": false,
78
+ "pk": false
79
+ },
80
+ {
81
+ "name": "balance",
82
+ "type": "TEXT",
83
+ "notnull": false,
84
+ "pk": false
85
+ },
86
+ {
87
+ "name": "housing",
88
+ "type": "TEXT",
89
+ "notnull": false,
90
+ "pk": false
91
+ },
92
+ {
93
+ "name": "loan",
94
+ "type": "TEXT",
95
+ "notnull": false,
96
+ "pk": false
97
+ },
98
+ {
99
+ "name": "contact",
100
+ "type": "TEXT",
101
+ "notnull": false,
102
+ "pk": false
103
+ },
104
+ {
105
+ "name": "day",
106
+ "type": "TEXT",
107
+ "notnull": false,
108
+ "pk": false
109
+ },
110
+ {
111
+ "name": "month",
112
+ "type": "TEXT",
113
+ "notnull": false,
114
+ "pk": false
115
+ },
116
+ {
117
+ "name": "duration",
118
+ "type": "TEXT",
119
+ "notnull": false,
120
+ "pk": false
121
+ },
122
+ {
123
+ "name": "campaign",
124
+ "type": "TEXT",
125
+ "notnull": false,
126
+ "pk": false
127
+ },
128
+ {
129
+ "name": "pdays",
130
+ "type": "TEXT",
131
+ "notnull": false,
132
+ "pk": false
133
+ },
134
+ {
135
+ "name": "previous",
136
+ "type": "TEXT",
137
+ "notnull": false,
138
+ "pk": false
139
+ },
140
+ {
141
+ "name": "poutcome",
142
+ "type": "TEXT",
143
+ "notnull": false,
144
+ "pk": false
145
+ },
146
+ {
147
+ "name": "y",
148
+ "type": "TEXT",
149
+ "notnull": false,
150
+ "pk": false
151
+ }
152
+ ],
153
+ "sample_rows": [
154
+ {
155
+ "age": "58",
156
+ "job": "management",
157
+ "marital": "married",
158
+ "education": "tertiary",
159
+ "default": "no",
160
+ "balance": "2143",
161
+ "housing": "yes",
162
+ "loan": "no",
163
+ "contact": "unknown",
164
+ "day": "5",
165
+ "month": "may",
166
+ "duration": "261",
167
+ "campaign": "1",
168
+ "pdays": "-1",
169
+ "previous": "0",
170
+ "poutcome": "unknown",
171
+ "y": "no"
172
+ },
173
+ {
174
+ "age": "44",
175
+ "job": "technician",
176
+ "marital": "single",
177
+ "education": "secondary",
178
+ "default": "no",
179
+ "balance": "29",
180
+ "housing": "yes",
181
+ "loan": "no",
182
+ "contact": "unknown",
183
+ "day": "5",
184
+ "month": "may",
185
+ "duration": "151",
186
+ "campaign": "1",
187
+ "pdays": "-1",
188
+ "previous": "0",
189
+ "poutcome": "unknown",
190
+ "y": "no"
191
+ },
192
+ {
193
+ "age": "33",
194
+ "job": "entrepreneur",
195
+ "marital": "married",
196
+ "education": "secondary",
197
+ "default": "no",
198
+ "balance": "2",
199
+ "housing": "yes",
200
+ "loan": "yes",
201
+ "contact": "unknown",
202
+ "day": "5",
203
+ "month": "may",
204
+ "duration": "76",
205
+ "campaign": "1",
206
+ "pdays": "-1",
207
+ "previous": "0",
208
+ "poutcome": "unknown",
209
+ "y": "no"
210
+ },
211
+ {
212
+ "age": "47",
213
+ "job": "blue-collar",
214
+ "marital": "married",
215
+ "education": "unknown",
216
+ "default": "no",
217
+ "balance": "1506",
218
+ "housing": "yes",
219
+ "loan": "no",
220
+ "contact": "unknown",
221
+ "day": "5",
222
+ "month": "may",
223
+ "duration": "92",
224
+ "campaign": "1",
225
+ "pdays": "-1",
226
+ "previous": "0",
227
+ "poutcome": "unknown",
228
+ "y": "no"
229
+ },
230
+ {
231
+ "age": "33",
232
+ "job": "unknown",
233
+ "marital": "single",
234
+ "education": "unknown",
235
+ "default": "no",
236
+ "balance": "1",
237
+ "housing": "no",
238
+ "loan": "no",
239
+ "contact": "unknown",
240
+ "day": "5",
241
+ "month": "may",
242
+ "duration": "198",
243
+ "campaign": "1",
244
+ "pdays": "-1",
245
+ "previous": "0",
246
+ "poutcome": "unknown",
247
+ "y": "no"
248
+ }
249
+ ]
250
+ }
251
+
252
+ Shortlisted templates:
253
+ [
254
+ {
255
+ "template_id": "tpl_tpch_relative_total_threshold",
256
+ "template_name": "Relative-to-Total Extreme Threshold",
257
+ "primary_family": "tail_rarity_structure",
258
+ "portability": "partial",
259
+ "sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;",
260
+ "required_roles": [
261
+ "group_col",
262
+ "measure_col"
263
+ ]
264
+ }
265
+ ]
266
+
267
+ Problem instance:
268
+ {
269
+ "dataset_id": "m8",
270
+ "question": "Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role count_distribution. Focus on group_col=education, measure_col=balance.",
271
+ "planned_template_id": "tpl_tpch_relative_total_threshold",
272
+ "bindings": {
273
+ "group_col": "education",
274
+ "measure_col": "balance",
275
+ "top_k": 13,
276
+ "top_n": 5,
277
+ "num_tiles": 10,
278
+ "percentile_value": 0.95,
279
+ "z_threshold": 2.0,
280
+ "fraction_threshold": 0.1,
281
+ "baseline_multiplier": 1.5,
282
+ "baseline_fraction": 0.1,
283
+ "min_group_size": 5,
284
+ "min_support": 5,
285
+ "measure_threshold": 1428.0,
286
+ "time_grain": "month",
287
+ "lookback_rows": 3,
288
+ "current_period_start": "'2024-01-01'",
289
+ "current_period_end": "'2024-04-01'",
290
+ "previous_period_start": "'2023-10-01'",
291
+ "previous_period_end": "'2024-01-01'",
292
+ "drift_ratio_threshold": 0.8
293
+ },
294
+ "can_vary": [],
295
+ "must_fix": [],
296
+ "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;"
297
+ }
298
+
299
+ Repair context:
300
+ {}
Query/sql/v2/runs/v2_cli_20260502_081223_f/m8/artifacts/v2q_m8_0d9ded01382f8312/cli/sql_prompt_attempt_2.txt ADDED
@@ -0,0 +1,300 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ You are generating one SQLite SELECT query for a single-table SQL QA task.
2
+ Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
3
+ Rules:
4
+ - Use only the provided table and columns.
5
+ - Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
6
+ - Prefer the planned template and bound roles when provided.
7
+ - Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
8
+ - Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
9
+ - Quote identifiers with double quotes.
10
+ - Return no markdown and no extra prose.
11
+
12
+ Dataset context:
13
+ Dataset context for SQL QA:
14
+ - dataset_id: m8
15
+ - dataset_name: Bank Marketing
16
+ - table_name: m8
17
+ - table_layout: single-table dataset (do not assume joins).
18
+ - row_semantics: One row is one tabular observation with 16 feature columns and target `y`.
19
+ - task_type: classification
20
+ - target_column: y
21
+ - main_row_count: 45211
22
+ - important_fields:
23
+ - age: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for age.
24
+ - job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for job.
25
+ - marital: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for marital.
26
+ - education: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for education.
27
+ - default: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for default.
28
+ - balance: role=feature, type=numeric. tags=['condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for balance.
29
+ - housing: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for housing.
30
+ - loan: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for loan.
31
+ - contact: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for contact.
32
+ - day: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for day.
33
+ - month: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for month.
34
+ - duration: role=feature, type=numeric. tags=['condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for duration.
35
+ - campaign: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for campaign.
36
+ - pdays: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for pdays.
37
+ - previous: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for previous.
38
+ - poutcome: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for poutcome.
39
+ - y: role=target, type=binary_target. tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for y.
40
+ - useful_field_combinations: [['age', 'job', 'y'], ['age', 'age', 'y']]
41
+ - fields_requiring_caution: ['y', 'balance', 'duration', 'pdays']
42
+ - source_url: https://archive.ics.uci.edu/dataset/222/bank+marketing
43
+
44
+ SQLite schema snapshot:
45
+ {
46
+ "table_name": "m8",
47
+ "quoted_table_name": "\"m8\"",
48
+ "row_count": 45211,
49
+ "columns": [
50
+ {
51
+ "name": "age",
52
+ "type": "TEXT",
53
+ "notnull": false,
54
+ "pk": false
55
+ },
56
+ {
57
+ "name": "job",
58
+ "type": "TEXT",
59
+ "notnull": false,
60
+ "pk": false
61
+ },
62
+ {
63
+ "name": "marital",
64
+ "type": "TEXT",
65
+ "notnull": false,
66
+ "pk": false
67
+ },
68
+ {
69
+ "name": "education",
70
+ "type": "TEXT",
71
+ "notnull": false,
72
+ "pk": false
73
+ },
74
+ {
75
+ "name": "default",
76
+ "type": "TEXT",
77
+ "notnull": false,
78
+ "pk": false
79
+ },
80
+ {
81
+ "name": "balance",
82
+ "type": "TEXT",
83
+ "notnull": false,
84
+ "pk": false
85
+ },
86
+ {
87
+ "name": "housing",
88
+ "type": "TEXT",
89
+ "notnull": false,
90
+ "pk": false
91
+ },
92
+ {
93
+ "name": "loan",
94
+ "type": "TEXT",
95
+ "notnull": false,
96
+ "pk": false
97
+ },
98
+ {
99
+ "name": "contact",
100
+ "type": "TEXT",
101
+ "notnull": false,
102
+ "pk": false
103
+ },
104
+ {
105
+ "name": "day",
106
+ "type": "TEXT",
107
+ "notnull": false,
108
+ "pk": false
109
+ },
110
+ {
111
+ "name": "month",
112
+ "type": "TEXT",
113
+ "notnull": false,
114
+ "pk": false
115
+ },
116
+ {
117
+ "name": "duration",
118
+ "type": "TEXT",
119
+ "notnull": false,
120
+ "pk": false
121
+ },
122
+ {
123
+ "name": "campaign",
124
+ "type": "TEXT",
125
+ "notnull": false,
126
+ "pk": false
127
+ },
128
+ {
129
+ "name": "pdays",
130
+ "type": "TEXT",
131
+ "notnull": false,
132
+ "pk": false
133
+ },
134
+ {
135
+ "name": "previous",
136
+ "type": "TEXT",
137
+ "notnull": false,
138
+ "pk": false
139
+ },
140
+ {
141
+ "name": "poutcome",
142
+ "type": "TEXT",
143
+ "notnull": false,
144
+ "pk": false
145
+ },
146
+ {
147
+ "name": "y",
148
+ "type": "TEXT",
149
+ "notnull": false,
150
+ "pk": false
151
+ }
152
+ ],
153
+ "sample_rows": [
154
+ {
155
+ "age": "58",
156
+ "job": "management",
157
+ "marital": "married",
158
+ "education": "tertiary",
159
+ "default": "no",
160
+ "balance": "2143",
161
+ "housing": "yes",
162
+ "loan": "no",
163
+ "contact": "unknown",
164
+ "day": "5",
165
+ "month": "may",
166
+ "duration": "261",
167
+ "campaign": "1",
168
+ "pdays": "-1",
169
+ "previous": "0",
170
+ "poutcome": "unknown",
171
+ "y": "no"
172
+ },
173
+ {
174
+ "age": "44",
175
+ "job": "technician",
176
+ "marital": "single",
177
+ "education": "secondary",
178
+ "default": "no",
179
+ "balance": "29",
180
+ "housing": "yes",
181
+ "loan": "no",
182
+ "contact": "unknown",
183
+ "day": "5",
184
+ "month": "may",
185
+ "duration": "151",
186
+ "campaign": "1",
187
+ "pdays": "-1",
188
+ "previous": "0",
189
+ "poutcome": "unknown",
190
+ "y": "no"
191
+ },
192
+ {
193
+ "age": "33",
194
+ "job": "entrepreneur",
195
+ "marital": "married",
196
+ "education": "secondary",
197
+ "default": "no",
198
+ "balance": "2",
199
+ "housing": "yes",
200
+ "loan": "yes",
201
+ "contact": "unknown",
202
+ "day": "5",
203
+ "month": "may",
204
+ "duration": "76",
205
+ "campaign": "1",
206
+ "pdays": "-1",
207
+ "previous": "0",
208
+ "poutcome": "unknown",
209
+ "y": "no"
210
+ },
211
+ {
212
+ "age": "47",
213
+ "job": "blue-collar",
214
+ "marital": "married",
215
+ "education": "unknown",
216
+ "default": "no",
217
+ "balance": "1506",
218
+ "housing": "yes",
219
+ "loan": "no",
220
+ "contact": "unknown",
221
+ "day": "5",
222
+ "month": "may",
223
+ "duration": "92",
224
+ "campaign": "1",
225
+ "pdays": "-1",
226
+ "previous": "0",
227
+ "poutcome": "unknown",
228
+ "y": "no"
229
+ },
230
+ {
231
+ "age": "33",
232
+ "job": "unknown",
233
+ "marital": "single",
234
+ "education": "unknown",
235
+ "default": "no",
236
+ "balance": "1",
237
+ "housing": "no",
238
+ "loan": "no",
239
+ "contact": "unknown",
240
+ "day": "5",
241
+ "month": "may",
242
+ "duration": "198",
243
+ "campaign": "1",
244
+ "pdays": "-1",
245
+ "previous": "0",
246
+ "poutcome": "unknown",
247
+ "y": "no"
248
+ }
249
+ ]
250
+ }
251
+
252
+ Shortlisted templates:
253
+ [
254
+ {
255
+ "template_id": "tpl_tpch_relative_total_threshold",
256
+ "template_name": "Relative-to-Total Extreme Threshold",
257
+ "primary_family": "tail_rarity_structure",
258
+ "portability": "partial",
259
+ "sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;",
260
+ "required_roles": [
261
+ "group_col",
262
+ "measure_col"
263
+ ]
264
+ }
265
+ ]
266
+
267
+ Problem instance:
268
+ {
269
+ "dataset_id": "m8",
270
+ "question": "Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role count_distribution. Focus on group_col=education, measure_col=balance.",
271
+ "planned_template_id": "tpl_tpch_relative_total_threshold",
272
+ "bindings": {
273
+ "group_col": "education",
274
+ "measure_col": "balance",
275
+ "top_k": 13,
276
+ "top_n": 5,
277
+ "num_tiles": 10,
278
+ "percentile_value": 0.95,
279
+ "z_threshold": 2.0,
280
+ "fraction_threshold": 0.1,
281
+ "baseline_multiplier": 1.5,
282
+ "baseline_fraction": 0.1,
283
+ "min_group_size": 5,
284
+ "min_support": 5,
285
+ "measure_threshold": 1428.0,
286
+ "time_grain": "month",
287
+ "lookback_rows": 3,
288
+ "current_period_start": "'2024-01-01'",
289
+ "current_period_end": "'2024-04-01'",
290
+ "previous_period_start": "'2023-10-01'",
291
+ "previous_period_end": "'2024-01-01'",
292
+ "drift_ratio_threshold": 0.8
293
+ },
294
+ "can_vary": [],
295
+ "must_fix": [],
296
+ "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;"
297
+ }
298
+
299
+ Repair context:
300
+ {}
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+ {"type":"turn.started"}
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+ {"type":"error","message":"Quota exceeded. Check your plan and billing details."}
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+ {
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+ "attempt": 1,
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+ "phase": "sql_generation",
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+ "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
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+ },
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+ "status": "failed",
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+ "error": "AI CLI command failed with exit code 1: ",
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+ "prompt_path": "cli/sql_prompt_attempt_1.txt",
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+ "response_path": "cli/sql_response_attempt_1.txt",
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+ "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
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+ "stderr_path": "cli/sql_stderr_attempt_1.txt"
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+ }
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+ {
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+ "attempt": 2,
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+ "phase": "sql_generation",
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+ "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
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+ },
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+ "status": "failed",
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+ "error": "AI CLI command failed with exit code 1: ",
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+ "prompt_path": "cli/sql_prompt_attempt_2.txt",
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+ "response_path": "cli/sql_response_attempt_2.txt",
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+ "raw_response_path": "cli/sql_response_attempt_2.raw.txt",
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+ }
Query/sql/v2/runs/v2_cli_20260502_081223_f/m8/artifacts/v2q_m8_0e094a2304cadc3c/cli/sql_prompt_attempt_1.txt ADDED
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1
+ You are generating one SQLite SELECT query for a single-table SQL QA task.
2
+ Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
3
+ Rules:
4
+ - Use only the provided table and columns.
5
+ - Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
6
+ - Prefer the planned template and bound roles when provided.
7
+ - Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
8
+ - Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
9
+ - Quote identifiers with double quotes.
10
+ - Return no markdown and no extra prose.
11
+
12
+ Dataset context:
13
+ Dataset context for SQL QA:
14
+ - dataset_id: m8
15
+ - dataset_name: Bank Marketing
16
+ - table_name: m8
17
+ - table_layout: single-table dataset (do not assume joins).
18
+ - row_semantics: One row is one tabular observation with 16 feature columns and target `y`.
19
+ - task_type: classification
20
+ - target_column: y
21
+ - main_row_count: 45211
22
+ - important_fields:
23
+ - age: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for age.
24
+ - job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for job.
25
+ - marital: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for marital.
26
+ - education: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for education.
27
+ - default: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for default.
28
+ - balance: role=feature, type=numeric. tags=['condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for balance.
29
+ - housing: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for housing.
30
+ - loan: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for loan.
31
+ - contact: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for contact.
32
+ - day: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for day.
33
+ - month: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for month.
34
+ - duration: role=feature, type=numeric. tags=['condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for duration.
35
+ - campaign: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for campaign.
36
+ - pdays: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for pdays.
37
+ - previous: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for previous.
38
+ - poutcome: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for poutcome.
39
+ - y: role=target, type=binary_target. tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for y.
40
+ - useful_field_combinations: [['age', 'job', 'y'], ['age', 'age', 'y']]
41
+ - fields_requiring_caution: ['y', 'balance', 'duration', 'pdays']
42
+ - source_url: https://archive.ics.uci.edu/dataset/222/bank+marketing
43
+
44
+ SQLite schema snapshot:
45
+ {
46
+ "table_name": "m8",
47
+ "quoted_table_name": "\"m8\"",
48
+ "row_count": 45211,
49
+ "columns": [
50
+ {
51
+ "name": "age",
52
+ "type": "TEXT",
53
+ "notnull": false,
54
+ "pk": false
55
+ },
56
+ {
57
+ "name": "job",
58
+ "type": "TEXT",
59
+ "notnull": false,
60
+ "pk": false
61
+ },
62
+ {
63
+ "name": "marital",
64
+ "type": "TEXT",
65
+ "notnull": false,
66
+ "pk": false
67
+ },
68
+ {
69
+ "name": "education",
70
+ "type": "TEXT",
71
+ "notnull": false,
72
+ "pk": false
73
+ },
74
+ {
75
+ "name": "default",
76
+ "type": "TEXT",
77
+ "notnull": false,
78
+ "pk": false
79
+ },
80
+ {
81
+ "name": "balance",
82
+ "type": "TEXT",
83
+ "notnull": false,
84
+ "pk": false
85
+ },
86
+ {
87
+ "name": "housing",
88
+ "type": "TEXT",
89
+ "notnull": false,
90
+ "pk": false
91
+ },
92
+ {
93
+ "name": "loan",
94
+ "type": "TEXT",
95
+ "notnull": false,
96
+ "pk": false
97
+ },
98
+ {
99
+ "name": "contact",
100
+ "type": "TEXT",
101
+ "notnull": false,
102
+ "pk": false
103
+ },
104
+ {
105
+ "name": "day",
106
+ "type": "TEXT",
107
+ "notnull": false,
108
+ "pk": false
109
+ },
110
+ {
111
+ "name": "month",
112
+ "type": "TEXT",
113
+ "notnull": false,
114
+ "pk": false
115
+ },
116
+ {
117
+ "name": "duration",
118
+ "type": "TEXT",
119
+ "notnull": false,
120
+ "pk": false
121
+ },
122
+ {
123
+ "name": "campaign",
124
+ "type": "TEXT",
125
+ "notnull": false,
126
+ "pk": false
127
+ },
128
+ {
129
+ "name": "pdays",
130
+ "type": "TEXT",
131
+ "notnull": false,
132
+ "pk": false
133
+ },
134
+ {
135
+ "name": "previous",
136
+ "type": "TEXT",
137
+ "notnull": false,
138
+ "pk": false
139
+ },
140
+ {
141
+ "name": "poutcome",
142
+ "type": "TEXT",
143
+ "notnull": false,
144
+ "pk": false
145
+ },
146
+ {
147
+ "name": "y",
148
+ "type": "TEXT",
149
+ "notnull": false,
150
+ "pk": false
151
+ }
152
+ ],
153
+ "sample_rows": [
154
+ {
155
+ "age": "58",
156
+ "job": "management",
157
+ "marital": "married",
158
+ "education": "tertiary",
159
+ "default": "no",
160
+ "balance": "2143",
161
+ "housing": "yes",
162
+ "loan": "no",
163
+ "contact": "unknown",
164
+ "day": "5",
165
+ "month": "may",
166
+ "duration": "261",
167
+ "campaign": "1",
168
+ "pdays": "-1",
169
+ "previous": "0",
170
+ "poutcome": "unknown",
171
+ "y": "no"
172
+ },
173
+ {
174
+ "age": "44",
175
+ "job": "technician",
176
+ "marital": "single",
177
+ "education": "secondary",
178
+ "default": "no",
179
+ "balance": "29",
180
+ "housing": "yes",
181
+ "loan": "no",
182
+ "contact": "unknown",
183
+ "day": "5",
184
+ "month": "may",
185
+ "duration": "151",
186
+ "campaign": "1",
187
+ "pdays": "-1",
188
+ "previous": "0",
189
+ "poutcome": "unknown",
190
+ "y": "no"
191
+ },
192
+ {
193
+ "age": "33",
194
+ "job": "entrepreneur",
195
+ "marital": "married",
196
+ "education": "secondary",
197
+ "default": "no",
198
+ "balance": "2",
199
+ "housing": "yes",
200
+ "loan": "yes",
201
+ "contact": "unknown",
202
+ "day": "5",
203
+ "month": "may",
204
+ "duration": "76",
205
+ "campaign": "1",
206
+ "pdays": "-1",
207
+ "previous": "0",
208
+ "poutcome": "unknown",
209
+ "y": "no"
210
+ },
211
+ {
212
+ "age": "47",
213
+ "job": "blue-collar",
214
+ "marital": "married",
215
+ "education": "unknown",
216
+ "default": "no",
217
+ "balance": "1506",
218
+ "housing": "yes",
219
+ "loan": "no",
220
+ "contact": "unknown",
221
+ "day": "5",
222
+ "month": "may",
223
+ "duration": "92",
224
+ "campaign": "1",
225
+ "pdays": "-1",
226
+ "previous": "0",
227
+ "poutcome": "unknown",
228
+ "y": "no"
229
+ },
230
+ {
231
+ "age": "33",
232
+ "job": "unknown",
233
+ "marital": "single",
234
+ "education": "unknown",
235
+ "default": "no",
236
+ "balance": "1",
237
+ "housing": "no",
238
+ "loan": "no",
239
+ "contact": "unknown",
240
+ "day": "5",
241
+ "month": "may",
242
+ "duration": "198",
243
+ "campaign": "1",
244
+ "pdays": "-1",
245
+ "previous": "0",
246
+ "poutcome": "unknown",
247
+ "y": "no"
248
+ }
249
+ ]
250
+ }
251
+
252
+ Shortlisted templates:
253
+ [
254
+ {
255
+ "template_id": "tpl_m4_quantile_tail_slice",
256
+ "template_name": "Quantile Tail Slice",
257
+ "primary_family": "tail_rarity_structure",
258
+ "portability": "partial",
259
+ "sql_skeleton": "WITH buckets AS (\n SELECT {measure_col},\n NTILE({num_tiles}) OVER (ORDER BY {measure_col} DESC) AS tail_bucket\n FROM {table}\n)\nSELECT {measure_col}\nFROM buckets\nWHERE tail_bucket = 1\nORDER BY {measure_col} DESC;",
260
+ "required_roles": [
261
+ "measure_col"
262
+ ]
263
+ }
264
+ ]
265
+
266
+ Problem instance:
267
+ {
268
+ "dataset_id": "m8",
269
+ "question": "Use template Quantile Tail Slice to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=campaign.",
270
+ "planned_template_id": "tpl_m4_quantile_tail_slice",
271
+ "bindings": {
272
+ "measure_col": "campaign",
273
+ "top_k": 12,
274
+ "top_n": 6,
275
+ "num_tiles": 10,
276
+ "percentile_value": 0.9,
277
+ "z_threshold": 2.0,
278
+ "fraction_threshold": 0.1,
279
+ "baseline_multiplier": 1.5,
280
+ "baseline_fraction": 0.1,
281
+ "min_group_size": 5,
282
+ "min_support": 5,
283
+ "measure_threshold": 3.0,
284
+ "time_grain": "month",
285
+ "lookback_rows": 3,
286
+ "current_period_start": "'2024-01-01'",
287
+ "current_period_end": "'2024-04-01'",
288
+ "previous_period_start": "'2023-10-01'",
289
+ "previous_period_end": "'2024-01-01'",
290
+ "drift_ratio_threshold": 0.8
291
+ },
292
+ "can_vary": [],
293
+ "must_fix": [],
294
+ "runtime_sql_skeleton": "WITH buckets AS (\n SELECT {measure_col},\n NTILE({num_tiles}) OVER (ORDER BY {measure_col} DESC) AS tail_bucket\n FROM {table}\n)\nSELECT {measure_col}\nFROM buckets\nWHERE tail_bucket = 1\nORDER BY {measure_col} DESC;"
295
+ }
296
+
297
+ Repair context:
298
+ {}
Query/sql/v2/runs/v2_cli_20260502_081223_f/m8/artifacts/v2q_m8_0e094a2304cadc3c/cli/sql_prompt_attempt_2.txt ADDED
@@ -0,0 +1,298 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ You are generating one SQLite SELECT query for a single-table SQL QA task.
2
+ Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
3
+ Rules:
4
+ - Use only the provided table and columns.
5
+ - Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
6
+ - Prefer the planned template and bound roles when provided.
7
+ - Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
8
+ - Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
9
+ - Quote identifiers with double quotes.
10
+ - Return no markdown and no extra prose.
11
+
12
+ Dataset context:
13
+ Dataset context for SQL QA:
14
+ - dataset_id: m8
15
+ - dataset_name: Bank Marketing
16
+ - table_name: m8
17
+ - table_layout: single-table dataset (do not assume joins).
18
+ - row_semantics: One row is one tabular observation with 16 feature columns and target `y`.
19
+ - task_type: classification
20
+ - target_column: y
21
+ - main_row_count: 45211
22
+ - important_fields:
23
+ - age: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for age.
24
+ - job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for job.
25
+ - marital: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for marital.
26
+ - education: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for education.
27
+ - default: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for default.
28
+ - balance: role=feature, type=numeric. tags=['condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for balance.
29
+ - housing: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for housing.
30
+ - loan: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for loan.
31
+ - contact: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for contact.
32
+ - day: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for day.
33
+ - month: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for month.
34
+ - duration: role=feature, type=numeric. tags=['condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for duration.
35
+ - campaign: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for campaign.
36
+ - pdays: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for pdays.
37
+ - previous: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for previous.
38
+ - poutcome: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for poutcome.
39
+ - y: role=target, type=binary_target. tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for y.
40
+ - useful_field_combinations: [['age', 'job', 'y'], ['age', 'age', 'y']]
41
+ - fields_requiring_caution: ['y', 'balance', 'duration', 'pdays']
42
+ - source_url: https://archive.ics.uci.edu/dataset/222/bank+marketing
43
+
44
+ SQLite schema snapshot:
45
+ {
46
+ "table_name": "m8",
47
+ "quoted_table_name": "\"m8\"",
48
+ "row_count": 45211,
49
+ "columns": [
50
+ {
51
+ "name": "age",
52
+ "type": "TEXT",
53
+ "notnull": false,
54
+ "pk": false
55
+ },
56
+ {
57
+ "name": "job",
58
+ "type": "TEXT",
59
+ "notnull": false,
60
+ "pk": false
61
+ },
62
+ {
63
+ "name": "marital",
64
+ "type": "TEXT",
65
+ "notnull": false,
66
+ "pk": false
67
+ },
68
+ {
69
+ "name": "education",
70
+ "type": "TEXT",
71
+ "notnull": false,
72
+ "pk": false
73
+ },
74
+ {
75
+ "name": "default",
76
+ "type": "TEXT",
77
+ "notnull": false,
78
+ "pk": false
79
+ },
80
+ {
81
+ "name": "balance",
82
+ "type": "TEXT",
83
+ "notnull": false,
84
+ "pk": false
85
+ },
86
+ {
87
+ "name": "housing",
88
+ "type": "TEXT",
89
+ "notnull": false,
90
+ "pk": false
91
+ },
92
+ {
93
+ "name": "loan",
94
+ "type": "TEXT",
95
+ "notnull": false,
96
+ "pk": false
97
+ },
98
+ {
99
+ "name": "contact",
100
+ "type": "TEXT",
101
+ "notnull": false,
102
+ "pk": false
103
+ },
104
+ {
105
+ "name": "day",
106
+ "type": "TEXT",
107
+ "notnull": false,
108
+ "pk": false
109
+ },
110
+ {
111
+ "name": "month",
112
+ "type": "TEXT",
113
+ "notnull": false,
114
+ "pk": false
115
+ },
116
+ {
117
+ "name": "duration",
118
+ "type": "TEXT",
119
+ "notnull": false,
120
+ "pk": false
121
+ },
122
+ {
123
+ "name": "campaign",
124
+ "type": "TEXT",
125
+ "notnull": false,
126
+ "pk": false
127
+ },
128
+ {
129
+ "name": "pdays",
130
+ "type": "TEXT",
131
+ "notnull": false,
132
+ "pk": false
133
+ },
134
+ {
135
+ "name": "previous",
136
+ "type": "TEXT",
137
+ "notnull": false,
138
+ "pk": false
139
+ },
140
+ {
141
+ "name": "poutcome",
142
+ "type": "TEXT",
143
+ "notnull": false,
144
+ "pk": false
145
+ },
146
+ {
147
+ "name": "y",
148
+ "type": "TEXT",
149
+ "notnull": false,
150
+ "pk": false
151
+ }
152
+ ],
153
+ "sample_rows": [
154
+ {
155
+ "age": "58",
156
+ "job": "management",
157
+ "marital": "married",
158
+ "education": "tertiary",
159
+ "default": "no",
160
+ "balance": "2143",
161
+ "housing": "yes",
162
+ "loan": "no",
163
+ "contact": "unknown",
164
+ "day": "5",
165
+ "month": "may",
166
+ "duration": "261",
167
+ "campaign": "1",
168
+ "pdays": "-1",
169
+ "previous": "0",
170
+ "poutcome": "unknown",
171
+ "y": "no"
172
+ },
173
+ {
174
+ "age": "44",
175
+ "job": "technician",
176
+ "marital": "single",
177
+ "education": "secondary",
178
+ "default": "no",
179
+ "balance": "29",
180
+ "housing": "yes",
181
+ "loan": "no",
182
+ "contact": "unknown",
183
+ "day": "5",
184
+ "month": "may",
185
+ "duration": "151",
186
+ "campaign": "1",
187
+ "pdays": "-1",
188
+ "previous": "0",
189
+ "poutcome": "unknown",
190
+ "y": "no"
191
+ },
192
+ {
193
+ "age": "33",
194
+ "job": "entrepreneur",
195
+ "marital": "married",
196
+ "education": "secondary",
197
+ "default": "no",
198
+ "balance": "2",
199
+ "housing": "yes",
200
+ "loan": "yes",
201
+ "contact": "unknown",
202
+ "day": "5",
203
+ "month": "may",
204
+ "duration": "76",
205
+ "campaign": "1",
206
+ "pdays": "-1",
207
+ "previous": "0",
208
+ "poutcome": "unknown",
209
+ "y": "no"
210
+ },
211
+ {
212
+ "age": "47",
213
+ "job": "blue-collar",
214
+ "marital": "married",
215
+ "education": "unknown",
216
+ "default": "no",
217
+ "balance": "1506",
218
+ "housing": "yes",
219
+ "loan": "no",
220
+ "contact": "unknown",
221
+ "day": "5",
222
+ "month": "may",
223
+ "duration": "92",
224
+ "campaign": "1",
225
+ "pdays": "-1",
226
+ "previous": "0",
227
+ "poutcome": "unknown",
228
+ "y": "no"
229
+ },
230
+ {
231
+ "age": "33",
232
+ "job": "unknown",
233
+ "marital": "single",
234
+ "education": "unknown",
235
+ "default": "no",
236
+ "balance": "1",
237
+ "housing": "no",
238
+ "loan": "no",
239
+ "contact": "unknown",
240
+ "day": "5",
241
+ "month": "may",
242
+ "duration": "198",
243
+ "campaign": "1",
244
+ "pdays": "-1",
245
+ "previous": "0",
246
+ "poutcome": "unknown",
247
+ "y": "no"
248
+ }
249
+ ]
250
+ }
251
+
252
+ Shortlisted templates:
253
+ [
254
+ {
255
+ "template_id": "tpl_m4_quantile_tail_slice",
256
+ "template_name": "Quantile Tail Slice",
257
+ "primary_family": "tail_rarity_structure",
258
+ "portability": "partial",
259
+ "sql_skeleton": "WITH buckets AS (\n SELECT {measure_col},\n NTILE({num_tiles}) OVER (ORDER BY {measure_col} DESC) AS tail_bucket\n FROM {table}\n)\nSELECT {measure_col}\nFROM buckets\nWHERE tail_bucket = 1\nORDER BY {measure_col} DESC;",
260
+ "required_roles": [
261
+ "measure_col"
262
+ ]
263
+ }
264
+ ]
265
+
266
+ Problem instance:
267
+ {
268
+ "dataset_id": "m8",
269
+ "question": "Use template Quantile Tail Slice to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=campaign.",
270
+ "planned_template_id": "tpl_m4_quantile_tail_slice",
271
+ "bindings": {
272
+ "measure_col": "campaign",
273
+ "top_k": 12,
274
+ "top_n": 6,
275
+ "num_tiles": 10,
276
+ "percentile_value": 0.9,
277
+ "z_threshold": 2.0,
278
+ "fraction_threshold": 0.1,
279
+ "baseline_multiplier": 1.5,
280
+ "baseline_fraction": 0.1,
281
+ "min_group_size": 5,
282
+ "min_support": 5,
283
+ "measure_threshold": 3.0,
284
+ "time_grain": "month",
285
+ "lookback_rows": 3,
286
+ "current_period_start": "'2024-01-01'",
287
+ "current_period_end": "'2024-04-01'",
288
+ "previous_period_start": "'2023-10-01'",
289
+ "previous_period_end": "'2024-01-01'",
290
+ "drift_ratio_threshold": 0.8
291
+ },
292
+ "can_vary": [],
293
+ "must_fix": [],
294
+ "runtime_sql_skeleton": "WITH buckets AS (\n SELECT {measure_col},\n NTILE({num_tiles}) OVER (ORDER BY {measure_col} DESC) AS tail_bucket\n FROM {table}\n)\nSELECT {measure_col}\nFROM buckets\nWHERE tail_bucket = 1\nORDER BY {measure_col} DESC;"
295
+ }
296
+
297
+ Repair context:
298
+ {}
Query/sql/v2/runs/v2_cli_20260502_081223_f/m8/artifacts/v2q_m8_0e094a2304cadc3c/cli/sql_response_attempt_1.raw.txt ADDED
@@ -0,0 +1,4 @@
 
 
 
 
 
1
+ {"type":"thread.started","thread_id":"019e4107-0562-7e21-8667-ff95d43673c4"}
2
+ {"type":"turn.started"}
3
+ {"type":"error","message":"Quota exceeded. Check your plan and billing details."}
4
+ {"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
Query/sql/v2/runs/v2_cli_20260502_081223_f/m8/artifacts/v2q_m8_0e094a2304cadc3c/cli/sql_response_attempt_1.txt ADDED
@@ -0,0 +1,4 @@
 
 
 
 
 
1
+ {"type":"thread.started","thread_id":"019e4107-0562-7e21-8667-ff95d43673c4"}
2
+ {"type":"turn.started"}
3
+ {"type":"error","message":"Quota exceeded. Check your plan and billing details."}
4
+ {"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
Query/sql/v2/runs/v2_cli_20260502_081223_f/m8/artifacts/v2q_m8_0e094a2304cadc3c/cli/sql_response_attempt_2.raw.txt ADDED
@@ -0,0 +1,4 @@
 
 
 
 
 
1
+ {"type":"thread.started","thread_id":"019e4107-169d-76b1-b322-762c822d4d62"}
2
+ {"type":"turn.started"}
3
+ {"type":"error","message":"Quota exceeded. Check your plan and billing details."}
4
+ {"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
Query/sql/v2/runs/v2_cli_20260502_081223_f/m8/artifacts/v2q_m8_0e094a2304cadc3c/cli/sql_response_attempt_2.txt ADDED
@@ -0,0 +1,4 @@
 
 
 
 
 
1
+ {"type":"thread.started","thread_id":"019e4107-169d-76b1-b322-762c822d4d62"}
2
+ {"type":"turn.started"}
3
+ {"type":"error","message":"Quota exceeded. Check your plan and billing details."}
4
+ {"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}
Query/sql/v2/runs/v2_cli_20260502_081223_f/m8/artifacts/v2q_m8_0e094a2304cadc3c/cli/sql_stderr_attempt_1.txt ADDED
File without changes
Query/sql/v2/runs/v2_cli_20260502_081223_f/m8/artifacts/v2q_m8_0e094a2304cadc3c/cli/sql_stderr_attempt_2.txt ADDED
File without changes