sanctions-er-anon/opensanctions_pairs

Sanctions Entity Resolution Benchmark

A benchmark for evaluating entity resolution (entity matching / record linkage) on sanctions and OSINT data. Each record is a pair of entities (persons or organizations) with a binary label indicating whether they refer to the same real-world entity.

This dataset is a snapshot of the OpenSanctions Pairs corpus (https://www.opensanctions.org/docs/opensource/pairs/), redistributed here in unmodified form to provide a stable, citable artefact for peer review and reproducibility. All credit for the underlying data belongs to OpenSanctions and the upstream curators whose source lists are aggregated by their cross-referencing pipeline. We claim no ownership of the data; this repository exists only to pin a specific snapshot used in our experiments.

Anonymised for double-blind peer review. Author and affiliation information will be added on de-anonymisation.

Code

Reference implementations, baselines, and evaluation scripts are available at the anonymised code repository:

https://anonymous.4open.science/r/OpenSanctionsPairs

Dataset Summary

• Pairs: 755,540
• Positive (same entity): 76.9%
• Negative (different entity): 23.1%
• Entity types: Person, Organization
• Languages: multilingual names; English metadata
• Source: OpenSanctions Pairs corpus, snapshot dated 2025-12-09 (https://www.opensanctions.org/docs/opensource/pairs/)

Files

File	Description	Size
pairs.json.gz	Full benchmark, JSON Lines (gzipped)	~390 MB
sample_1000.json	1,000-pair representative sample (see below)	~3 MB

1,000-pair representative sample

sample_1000.json is a label-stratified random subsample of the full 755,540-pair corpus, provided for quick inspection, smoke tests, and compliance review without downloading the full archive. It was generated by scripts/create_proper_sample.py with seed 42, as follows:

1. Stratification. The corpus is split by the analyst judgement field into positives and negatives. Quotas are set to match the corpus class balance: 76.9% positive (769 pairs) and 23.1% negative (231 pairs), giving 1,000 pairs total.
2. Random sampling. Within each class, pairs are drawn uniformly at random without replacement using Python's random.sample under a fixed seed (42).
3. Shuffling. The two class samples are concatenated and globally shuffled with the same seed so positives and negatives are interleaved rather than blocked.
4. Validation. The script verifies that (a) the realised positive ratio is within 0.02 of the 76.9% target, (b) the number of judgement-label runs is at least 70% of the expected value for an i.i.d. shuffle, and (c) every non-overlapping 100-pair window contains both classes. The released sample passes all three checks.
5. Provenance. The output JSON carries a metadata block recording the seed, target and realised positive ratios, source filename (pairs-20251209.json.gz), and creation timestamp, so the sample can be regenerated bit-for-bit from the public corpus.

The sample preserves the corpus-level class balance and is drawn uniformly across the entire labeled pool, so it inherits the schema mix, source-heterogeneity, and script-coverage distributions of the full release without any further filtering or reweighting. Full reproduction code is at https://anonymous.4open.science/r/OpenSanctionsPairs/.

Record Schema

One JSON object per line:

{
  "left": {
    "id": "ofac-40604",
    "caption": "Aliasghar Norouzi",
    "schema": "Person",
    "properties": {
      "alias": ["Ali Asghar Norowzi"],
      "birthDate": ["1962-11-11"],
      "nationality": ["ir"]
    }
  },
  "right": {
    "id": "...",
    "caption": "...",
    "schema": "Person",
    "properties": { "...": ["..."] }
  },
  "judgement": "positive"
}

• left, right: entity records with stable IDs, a display caption, a schema (Person or Organization), and a properties dict of attribute to list-of-string-values
• judgement: "positive" (same entity) or "negative" (different)

properties keys vary by entity but commonly include alias, birthDate, nationality, country, address, idNumber, registrationNumber, incorporationDate.

Loading

from datasets import load_dataset

ds = load_dataset(
    "sanctions-er-anon/opensanctions_pairs",
    data_files="pairs.json.gz",
    split="train",
)
print(ds[0])

Or stream without download:

ds = load_dataset(
    "sanctions-er-anon/opensanctions_pairs",
    data_files="pairs.json.gz",
    split="train",
    streaming=True,
)

Intended Uses

• Benchmarking entity resolution / matching systems
• Evaluating LLM and fine-tuned model performance on real-world noisy multilingual entity records
• Studying compliance, sanctions screening, and OSINT use cases

Out-of-Scope and Limitations

• Entities are drawn from sanctions, watchlist, and PEP sources; distribution is not representative of general-purpose record linkage (commercial CRM, healthcare, etc.).
• Labels reflect cross-referencing decisions made by upstream curators and may contain noise.
• Names are romanised / transliterated inconsistently across sources; some pairs are intentionally challenging due to script and transliteration variation.
• The benchmark should not be used as a basis for real-world sanctions enforcement or watchlist screening without human review.

Ethical Considerations

The underlying data covers individuals and organisations on public sanctions and watchlists. All records are already public. Users should not redistribute the data in contexts that imply guilt or wrongdoing, and should follow the source licence and OpenSanctions terms of use.

Licence

Released under CC-BY-NC-4.0, inheriting the licence of the upstream OpenSanctions data. The dataset is for non-commercial research and evaluation use only; commercial use requires a separate licence from OpenSanctions (https://www.opensanctions.org/licensing/).

Citation

If you use this dataset, please cite OpenSanctions as the source of the underlying data. A pointer to the specific snapshot:

OpenSanctions. Pairs: Cross-Referencing Training Data.
https://www.opensanctions.org/docs/opensource/pairs/
Snapshot dated 2025-12-09.

The benchmark paper citation will be added upon de-anonymisation.

Source and Credit

This dataset is a verbatim snapshot of the OpenSanctions Pairs corpus, taken on 2025-12-09:

• Pairs documentation: https://www.opensanctions.org/docs/opensource/pairs/
• OpenSanctions homepage: https://www.opensanctions.org/
• Source code and pipeline: https://github.com/opensanctions/opensanctions
• Licensing: https://www.opensanctions.org/licensing/

OpenSanctions is the originator and maintainer of the data. The pairs are produced by their cross-referencing pipeline, which aggregates public sanctions lists, PEP registers, and watchlists from numerous upstream authorities. Please cite OpenSanctions in any work that uses this dataset, and consult their site for the canonical, continuously updated version.

We host this snapshot solely to provide a stable, immutable reference for reproducible benchmarking, since the live OpenSanctions data changes daily.