arsalan-anwari/zer-data

Dutch Law Enforcement Entity Resolution Benchmark

A collection of synthetic benchmark datasets for entity resolution and record linkage research, modelled on real Dutch and EU/EEA law enforcement data schemas.

All data is 100% synthetic. No records correspond to real persons, vehicles, phone numbers, transactions, or criminal histories. Every name, date, plate number, IBAN, IMSI, and identifier is procedurally generated. The datasets are freely usable, shareable, and publishable without privacy or legal concerns.

Datasets are generated from real-world schemas (KvK Handelsregister, Basisregistratie Personen, SIS II Article 26/36 specification, Dutch ANPR kentekencamera feed, HKS criminal antecedents, Interpol I-24/7 notice schema, telecom CDR / ETSI LI standard, FIU-NL GTR) combined with authentic Dutch demographic distributions from CBS (Centraal Bureau voor de Statistiek) statistics. Error patterns — name variants, OCR confusion, romanization differences, estimated dates of birth, SIM swap events — are injected to replicate the specific challenges found in Dutch and cross-Schengen identity data.

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Why this dataset exists

Standard entity resolution benchmarks (DBLP-ACM, Febrl, Cora) do not reflect the challenges of Dutch law enforcement data:

• Dutch name complexity — tussenvoegsel prefixes (van, de, van der, van den, van 't) are frequently omitted, abbreviated, or formatted inconsistently across registrations
• Multicultural name variation — Dutch-Moroccan and Dutch-Turkish communities produce names with authentic spelling variants (Mohammed / Mohamed / Muhammad, dropped Turkish diacritics like Çelik → Celik)
• Cross-Schengen identity — SIS II entries are registered by different member states in different scripts; the same person may appear as Benabdallah Fatima in one state and Fatima Benabdallah in another, with Arabic-script aliases alongside Latin ones
• ANPR OCR errors — Dutch highway cameras produce predictable single-character plate confusions (0↔O, 1↔I, 8↔B, 5↔S, 2↔Z) that require fuzzy matching rather than exact lookup
• Estimated dates of birth — asylum and immigration records frequently use YYYY-01-01 (January 1st fallback) when the exact birth date is unknown, creating year-boundary matching challenges
• Telecom identity churn — SIM swap events mean the same MSISDN can be associated with different persons over time, while IMEI reuse links different subscribers through a shared device
• Financial network structure — money mule rings and structuring patterns require graph-aware resolution rather than pairwise comparison

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Available sizes

Each dataset type is available at multiple scale tiers:

Tier	Records per provider	Suitable for
50K	50,000	Correctness checks, unit tests
100K	100,000	Development and integration testing
250K	250,000	Small-scale benchmarking
500K	500,000	Performance benchmarking
1M	1,000,000	Large-scale throughput and memory tests

All tiers use deterministic seeds so results are reproducible across runs.

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Datasets

1. KvK Director Records

Company director registrations from the Dutch Chamber of Commerce (Kamer van Koophandel). The same person often directs multiple companies, with each registration entered independently — producing name variants, formatting differences, and address drift over time. Directors of BV and NV companies are registered as bestuurders; sole proprietors (Eenmanszaak) are both owner and entity.

Files: kvk_director_flat.csv, kvk_director_extract.json, ground_truth_pairs.csv

Schema — kvk_director_flat.csv:

Field	Type	Description
kvkNummer	string	Chamber of Commerce registration number (8 digits)
rechtsvorm	categorical	Legal form (Eenmanszaak, BV, NV, VOF, …)
handelsnaam	string	Company trading name
voornamen	string	Director given names
tussenvoegsel	string	Dutch name infix (van, de, van der, …) — may be empty
achternaam	string	Director surname
geboortedatum	date	Date of birth (ISO 8601)
geboorteplaats	string	Place of birth
woonplaats	string	City of residence
postcode	string	Dutch postcode (DDDDLL)
straatnaam	string	Street name
huisnummer	string	House number
sbiCode	string	SBI sector classification code
aantalWerkzamePersonen	integer	Number of employees

Ground truth format: kvk_a, kvk_b, is_match, match_type

Match types: same_person_multi_company, duplicate_entry, name_variant

Default scale: ~2,000 director records · ~1,300 true pairs

Injected error patterns:

• Tussenvoegsel variants: van der / v/d / vd / omitted entirely
• Abbreviated given names: Johannes → J. or J.A.
• Case and whitespace inconsistencies across independent registrations
• Address drift between an individual's multiple company registrations

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2. BRP Population Register

Person records modelled on the Basisregistratie Personen (BRP), the Dutch municipal population register. Every resident — Dutch national or foreign national with a registered address — has a BRP entry. The dataset tests string similarity matching, address normalisation, date of birth transcription errors, and dual-nationality scoring.

Files: brp_persons.csv, ground_truth_pairs.csv

Schema — brp_persons.csv:

Field	Type	Description
bsn	string	Burgerservicenummer — passes 11-test (elfproef)
voornamen	string	Given names
tussenvoegsel	string	Tussenvoegsel — may be empty
achternaam	string	Surname
geboortedatum	date	Date of birth (ISO 8601)
geboorteplaats	string	Municipality of birth
geboorteland	string	Country of birth (ISO 3166-1 alpha-2)
nationaliteit	string	Nationality; dual nationality separated by / (e.g. NL/MA)
geslacht	categorical	Sex (M, V)
straatnaam	string	Street name
huisnummer	string	House number (may include toevoeging: 12A, 12bis)
postcode	string	Dutch postcode (DDDDLL)
woonplaats	string	Municipality of residence
verblijfstitel	string	Residence permit type (blank for Dutch nationals)

Ground truth format: bsn_a, bsn_b, is_match, match_type

Match types: address_move, name_variant, dob_error, nationality_partial

Default scale: ~11,000 records · ~1,000 true pairs

Injected error patterns:

• Address moves: same person registered at two different addresses across time
• Name variants: abbreviated given names, tussenvoegsel capitalised or omitted
• Date of birth transcription errors: day and month transpositions
• Partial nationality: one record shows dual nationality, the other only one

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3. SIS II Wanted and Missing Persons

Person records modelled on the Schengen Information System II (SIS II) Article 26 (wanted persons) and Article 36 (missing persons) schemas. The key challenge is cross-Schengen alias pairs: the same individual entered by different member states using different name order conventions, romanizations, or scripts.

Files: sis_persons.csv, ground_truth_alias_pairs.csv

Schema — sis_persons.csv:

Field	Type	Description
sis_id	string	SIS II alert identifier (8 hex chars)
categorie	categorical	Alert category (wanted_person, missing_person)
voornamen	string	Given names
achternaam	string	Surname
alias_namen	string	Pipe-delimited alias list (alias1|alias2|alias3)
geboortedatum	date	Date of birth; YYYY-01-01 for estimated DOBs
geboorteplaats	string	Place of birth
geboorteland	string	Country of birth (ISO 3166-1 alpha-2)
nationaliteit	string	Nationality (ISO 3166-1 alpha-2)
geslacht	categorical	Sex (M, V)
document_type	categorical	Document type (Paspoort, Identiteitskaart, …)
document_nummer	string	Document number
uitgevend_land	string	Issuing country (ISO 3166-1 alpha-2)
lengte	integer	Height in cm
haarkleur	categorical	Hair colour
oogkleur	categorical	Eye colour
aanmakende_staat	string	Schengen member state that created the alert

Ground truth format: sis_id_a, sis_id_b, is_match, match_type

Match types: alias_romanization_variant, name_transposition, estimated_dob_variant, cross_script_alias

Default scale: ~9,000 records · ~4,000 true pairs

Injected error patterns:

• Name transposition: Benabdallah Fatima ↔ Fatima Benabdallah (given/surname order varies by country convention)
• Romanization variants: Mohammed / Mohamed / Muhammed; Youssef / Yousef / Yusuf
• Estimated date of birth: ~30% of records use YYYY-01-01 (January 1st fallback)
• Non-Latin script aliases: Arabic-script aliases alongside Latin primary names
• Document number prefix variation: same serial with different country-code prefixes

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4. HKS Criminal Antecedents

Records modelled on the Herkenningsdienstsysteem (HKS), the Dutch National Police criminal antecedents system. HKS records are subject-oriented rather than incident-oriented: a single individual may appear under their registered name in the BRP while their HKS entry carries a street alias or romanization variant. The BSN is present for ~60% of records.

Files: hks_records.csv, ground_truth_pairs.csv

Schema — hks_records.csv:

Field	Type	Description
hks_id	string	HKS record identifier
voornamen	string	Given names (may be alias)
achternaam	string	Surname (may be alias)
tussenvoegsel	string	Tussenvoegsel — may be empty
alias_namen	string	Pipe-delimited street and document aliases
geboortedatum	date	Date of birth (ISO 8601)
geboorteplaats	string	Place of birth
geboorteland	string	Country of birth (ISO 3166-1 alpha-2)
nationaliteit	string	Nationality (ISO 3166-1 alpha-2)
geslacht	categorical	Sex (M, V)
bsn	string	BSN — present in ~60% of records; empty otherwise
document_type	categorical	Identity document type
document_nummer	string	Document number
lengte	integer	Height in cm
haarkleur	categorical	Hair colour
oogkleur	categorical	Eye colour
delict_types	string	Pipe-delimited offence categories

Ground truth format: hks_id_a, hks_id_b, is_match, match_type

Match types: alias_vs_registered_name, bsn_linked, street_alias, romanization_variant

Default scale: ~6,400 records · ~1,400 true pairs

Injected error patterns:

• Registered name vs. street alias: BRP name differs from HKS operational alias
• BSN linkage: direct BSN match despite all name fields differing
• Street alias abbreviation: Mohammed El Haddad → Mo Haddad
• Romanization: Arabic-origin surnames appear in multiple Latin transcriptions

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5. Interpol Notices

Records modelled on the Interpol I-24/7 notice schema (Red, Blue, Yellow, and Green notices). The primary cross-source challenge is that the same individual may appear in SIS II under a Dutch or German romanization convention and in Interpol under a French or English convention — producing the same person with systematically different spellings across two datasets.

Files: interpol_notices.csv, ground_truth_pairs.csv

Schema — interpol_notices.csv:

Field	Type	Description
notice_id	string	Interpol notice identifier
notice_type	categorical	Notice colour (Red, Blue, Yellow, Green)
voornamen	string	Given names
achternaam	string	Surname
alias_namen	string	Pipe-delimited aliases
geboortedatum	date	Date of birth (ISO 8601)
geboorteplaats	string	Place of birth
geboorteland	string	Country of birth (ISO 3166-1 alpha-2)
nationaliteit	string	Nationality (ISO 3166-1 alpha-2)
geslacht	categorical	Sex (M, V)
talen	string	Pipe-delimited languages spoken
document_type	categorical	Identity document type
document_nummer	string	Document number
lengte	integer	Height in cm
gewicht	integer	Weight in kg
haarkleur	categorical	Hair colour
oogkleur	categorical	Eye colour
aanvragende_landen	string	Pipe-delimited requesting member countries

Ground truth format: notice_id_a, notice_id_b, is_match, match_type

Match types: cross_source_romanization_variant, alias_match, partial_dob

Default scale: ~3,600 records · ~630 true pairs

Injected error patterns:

• Cross-source romanization: different transliteration conventions per requesting country
• Name-order transposition between Western (given-surname) and Eastern (surname-given) conventions
• Partial date of birth: year only, or year + month, without day

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6. ANPR Vehicle Passages

Highway camera passage events modelled on the Dutch ANPR (Automatische NummerPlaat Herkenning) kentekencamera feed operated by Dienst Verkeerspolitie. The primary challenge is single-character OCR confusion: a camera may read CX-I80-W when the true plate is CX-180-W. Vehicle trajectories link the same vehicle across multiple cameras on a corridor.

Files: anpr_passages.csv, ground_truth_vehicle_pairs.csv

Schema — anpr_passages.csv:

Field	Type	Description
passage_id	string	Passage identifier ({8hex}-{3hex})
tijdstip	datetime	Passage timestamp (ISO 8601 with time)
camera_id	string	Camera identifier (CAM-{road}-{seq}, e.g. CAM-A12-001)
weg	string	Highway designation (A1, A2, A4, A10, A12, A20, …)
lat	float	Camera latitude (decimal degrees, WGS84)
lon	float	Camera longitude (decimal degrees, WGS84)
rijrichting	categorical	Direction of travel (N, Z, O, W)
kenteken	string	License plate as read by OCR — may contain a single-character error
kenteken_betrouwbaarheid	integer	OCR confidence score 0–100
merk_herkend	string	Vehicle make (if MMR camera detected it)
kleur_herkend	categorical	Vehicle colour (if detected)
snelheid_kmh	integer	Speed in km/h

Ground truth format: passage_id_a, kenteken_true, kenteken_ocr, is_match, match_type

Match types: ocr_confusion (with specific substitution noted, e.g. 1_to_I)

Default scale: ~50,000 passages · ~2,490 true pairs

Injected OCR confusion pairs (single substitution per plate, bidirectional):

Character read	True character
O	0
I	1
B	8
S	5
Z	2

Dutch license plate formats used: DD-LLL-D, LL-DD-LL, DD-LL-DD, L-DDD-LL, LL-DDD-L (current RDW series). Camera coordinates are sampled from bounding boxes around real Dutch highway corridors (A1, A2, A4, A10, A12, A15, A20, A28).

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7. CDR Call Detail Records

Call detail records modelled on the telecom CDR / ETSI LI standard as used in Dutch lawful interception. The primary resolution challenge is identity across the MSISDN/IMSI/IMEI triple: a SIM swap leaves the MSISDN the same but changes the IMSI; a device sale leaves the IMEI the same but changes both MSISDN and IMSI. Graph-annotated variants include structured subscriber clusters (10–40 nodes, 85% intra-cluster calling) for community detection tests.

Files: cdr_records.csv, ground_truth_clusters.csv

Schema — cdr_records.csv:

Field	Type	Description
cdr_id	string	Record identifier (UUID)
tijdstip_start	datetime	Call start timestamp (ISO 8601)
duur_seconden	integer	Call duration in seconds
call_type	categorical	Call type (voice, sms, data)
msisdn_a	string	Calling party MSISDN (E.164, +316…)
imsi_a	string	IMSI of calling party
imei_a	string	IMEI of calling device (15 digits)
cell_id_a_start	string	Cell tower ID at call start
msisdn_b	string	Called party MSISDN (E.164)
imsi_b	string	IMSI of called party
imei_b	string	IMEI of called device
carrier_a	string	Carrier of calling party (KPN, T-Mobile, Vodafone, …)
carrier_b	string	Carrier of called party

Ground truth format: msisdn, imsi, cluster_id, is_hub

The ground truth table is a cluster-membership list rather than a pair list. Each row assigns a subscriber (MSISDN + IMSI) to a cluster and flags whether they are the communication hub of that cluster.

Default scale: ~50,000 records · ~6,300 cluster-membership rows

Injected identity patterns:

• SIM swap: same MSISDN, old IMSI replaced by new IMSI mid-dataset (~15% of clusters)
• IMEI reuse: same device IMEI appears under two different MSISDNs (device resold / shared)
• Burner handset: new IMEI + new MSISDN registered within existing communication cluster

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8. SIM Subscriber Registrations

Subscriber registration records modelled on Dutch telecom subscriber databases as maintained by KPN, T-Mobile, Vodafone, Tele2, and associated MVNOs. Pairs the identity data collected at point-of-sale (name, date of birth, document number) with the technical subscriber identity (MSISDN, IMSI, ICCID). A companion swap-events table records IMSI changes on the same SIM card.

Files: sim_subscribers.csv, sim_swap_events.csv, ground_truth_swaps.csv

Schema — sim_subscribers.csv:

Field	Type	Description
sim_id	string	Internal subscriber record identifier
msisdn	string	Mobile number (E.164, +316…)
imsi	string	International Mobile Subscriber Identity
iccid	string	SIM card serial number (19 digits)
carrier	string	Carrier name
contract_type	categorical	Contract type (prepaid, postpaid)
activatiedatum	date	SIM activation date (ISO 8601)
voornamen	string	Subscriber given names
achternaam	string	Subscriber surname
geboortedatum	date	Date of birth
nationaliteit	string	Nationality (ISO 3166-1 alpha-2)
document_type	categorical	Identity document used at registration
document_nummer	string	Document number
bsn	string	BSN — present for postpaid contracts; empty for prepaid

Schema — sim_swap_events.csv:

Field	Type	Description
swap_id	string	Swap event identifier
msisdn	string	MSISDN on which the swap occurred
oude_imsi	string	IMSI before the swap
nieuwe_imsi	string	IMSI after the swap
tijdstip	datetime	Timestamp of the swap event

Ground truth format: sim_id_a, sim_id_b, msisdn, is_match, match_type

Match types: sim_swap_same_person, shared_device, identity_document_reuse

Default scale: ~10,800 subscriber records · ~800 swap events · ~800 true pairs

Injected identity patterns:

• SIM swap: same subscriber activates a replacement SIM — same MSISDN, new ICCID and IMSI
• Prepaid registration gap: prepaid subscribers registered without BSN, matched via name + DOB
• Document reuse: same identity document number associated with two subscriber registrations (stolen / cloned document scenario)

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9. FIU Unusual Transaction Reports

Transaction records modelled on FIU-NL (Financial Intelligence Unit Nederland) Geldtransactie Reporting (GTR) reports, as required under the Wwft (Anti-Money Laundering Act). Graph-annotated variants include structured money mule rings (5–15 accounts per ring, feeder → mule hub → offshore IBAN pattern) for graph analytics and network-aware entity resolution.

Files: fiu_transactions.csv, ground_truth_clusters.csv

Schema — fiu_transactions.csv:

Field	Type	Description
melding_id	string	FIU report identifier
tijdstip	datetime	Transaction timestamp (ISO 8601)
transaction_type	categorical	Transaction type (overboeking, contant, crypto, betaalverzoek)
iban_sender	string	Sender IBAN (NL format: NL{check}{bank}{account})
naam_sender	string	Sender name
geboortedatum_sender	date	Sender date of birth
nationaliteit_sender	string	Sender nationality (ISO 3166-1 alpha-2)
iban_receiver	string	Receiver IBAN
naam_receiver	string	Receiver name
land_receiver	string	Receiver country (ISO 3166-1 alpha-2)
bedrag_eur	float	Transaction amount in EUR
reden_melding	categorical	Reason for unusual transaction report

Ground truth format: iban, cluster_id, rol, is_mule

The ground truth table is a cluster-membership list. Each row assigns an IBAN to a money mule ring (cluster_id), specifies the role (feeder, hub, offshore), and flags whether the account is a mule account.

Default scale: ~20,000 transaction records · ~490 ring-membership rows

Injected network patterns:

• Structuring: large amounts split across multiple small transfers just below reporting thresholds
• Layering: feeder accounts transfer to a central mule hub, which then moves funds offshore
• Name discrepancy: IBAN holder name does not match the name on the FIU report (third-party account)
• IBAN prefix clustering: offshore IBANs cluster around a small set of high-risk jurisdictions

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Demographic composition

Name distributions reflect Dutch population demographics (CBS 2025, source: cbs.nl — Population by origin):

Name origin	Share	Approx. number
Dutch (both parents born in NL)	~72%	~13.0 million
Other European	~8–9%	~1.5 million
Moroccan	~2.4%	~433,000
Turkish	~2.4%	~435,000
Surinamese	~2.0%	~360,000
Indonesian	~2.0%	~360,000
Other / rest of world	~8–9%	~1.5 million

Arabic and Turkish names are generated with authentic spelling variants to replicate the romanization challenges found in real Dutch administrative data. SIS II and Interpol records include a proportion of non-Latin script aliases.

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File formats

All files are UTF-8 encoded CSV with a header row. Pipe (|) is used as the internal delimiter for multi-valued fields (e.g. alias_namen, delict_types). Ground truth files always include an is_match column (True / False) and a match_type column describing the specific error pattern injected. Cluster ground truth files use a membership-list format rather than a pair list.

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License

All data is fully synthetic and released under CC BY 4.0. No real personal data, criminal records, vehicle information, or financial records are included. Attribution: cite this dataset as Dutch Law Enforcement Entity Resolution Benchmark (synthetic), arsalan-anwari, 2025.