OpenSanctions Pairs: Large-Scale Entity Matching with LLMs

• URL: http://arxiv.org/abs/2603.11051v1
• Date: Tue, 24 Feb 2026 06:25:49 GMT
• Title: OpenSanctions Pairs: Large-Scale Entity Matching with LLMs
• Abstract summary: We release OpenSanctions Pairs, a large-scale entity matching benchmark derived from real-world international sanctions aggregation and analyst deduplication.<n>The dataset contains 755,540 labeled pairs spanning 293 heterogeneous sources across 31 countries.<n>Off-the-shelf LLMs substantially outperform the production rule-based baseline.
• Score: 0.9131359219276399
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We release OpenSanctions Pairs, a large-scale entity matching benchmark derived from real-world international sanctions aggregation and analyst deduplication. The dataset contains 755,540 labeled pairs spanning 293 heterogeneous sources across 31 countries, with multilingual and cross-script names, noisy and missing attributes, and set-valued fields typical of compliance workflows. We benchmark a production rule-based matcher (nomenklatura RegressionV1 algorithm) against open- and closed-source LLMs in zero- and few-shot settings. Off-the-shelf LLMs substantially outperform the production rule-based baseline (91.33\% F1), reaching up to 98.95\% F1 (GPT-4o) and 98.23\% F1 with a locally deployable open model (DeepSeek-R1-Distill-Qwen-14B). DSPy MIPROv2 prompt optimization yields consistent but modest gains, while adding in-context examples provides little additional benefit and can degrade performance. Error analysis shows complementary failure modes: the rule-based system over-matches (high false positives), whereas LLMs primarily fail on cross-script transliteration and minor identifier/date inconsistencies. These results indicate that pairwise matching performance is approaching a practical ceiling in this setting, and motivate shifting effort toward pipeline components such as blocking, clustering, and uncertainty-aware review. Code available at https://github.com/chansmi/OSINT_entity_resolution

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