Card: definition_excerpts column in taxonomy

README.md

@@ -148,7 +148,7 @@ configs:
 
 1,420 publicly published international development evaluation reports (about 40 organisations), with document labels and excerpt tags. Document labels come in two sets: silver labels from GLM-5.3-Flash (default columns) and the original pipeline labels (`*_pipeline`). Built for [baobab-tech/decision-models-experiments](https://github.com/baobab-tech/decision-models-experiments): experiment [01](https://github.com/baobab-tech/decision-models-experiments/tree/main/experiments/01-many-option-classification) (zero-shot many-option classification) and [02](https://github.com/baobab-tech/decision-models-experiments/tree/main/experiments/02-fine-tuning) (fine-tuning).
 
-Reports were converted from PDF with Docling and labelled by an LLM ingestion pipeline. The dataset is built by [`build_evaluation_docs_dataset.py`](https://github.com/baobab-tech/decision-models-experiments/blob/main/experiments/common/build_evaluation_docs_dataset.py) from `baobabtech/evalexplorer-data` revision `5315eab`; label names and definitions come from the pipeline's taxonomy and classification prompt.
+Reports were converted from PDF with Docling and labelled by an LLM ingestion pipeline. The dataset is built by [`build_evaluation_docs_dataset.py`](https://github.com/baobab-tech/decision-models-experiments/blob/main/experiments/common/build_evaluation_docs_dataset.py) from `baobabtech/evalexplorer-data` revision `5315eab`; label names and definitions come from the pipeline's taxonomy and its document and excerpt prompts (eval-explorer commit `6ae90f7` for the excerpt prompts).
 
 ```python
 from datasets import load_dataset
@@ -191,7 +191,7 @@ Splits are by document (80/10/10 on a hash of `document_id`), shared by both con
 
 ### `taxonomy`
 
-`field`, `code`, `label`, `definition` (document fields only), `region` (countries only), `in_documents`, `in_excerpts`. The two flags mark the codes that occur in the silver or pipeline labels; experiments ask over those sets.
+`field`, `code`, `label`, `definition` (document fields only, from the document classification prompt), `definition_excerpts` (themes and methods, from the excerpt extract-and-classify prompts; longer theme definitions), `region` (countries only), `in_documents`, `in_excerpts`. The two flags mark the codes that occur in the silver or pipeline labels; experiments ask over those sets.
 
 ## Labels