# OpenSanctions Pairs: Large-Scale Entity Matching with LLMs

arXiv: https://arxiv.org/abs/2603.11051 · HTML: https://mlsanity.com/paper/2603.11051/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 43 references that are in the map.
- First posted on arXiv 24 Feb 2026.

## Builds on (most cited first, 17 of 17)

- [Deep Entity Matching with Pre-Trained Language Models](https://mlsanity.com/paper/2004.00584/) (2020)
- [Neural Networks for Entity Matching: A Survey](https://mlsanity.com/paper/2010.11075/) (2020)
- [Match, Compare, or Select? An Investigation of Large Language Models for Entity Matching](https://mlsanity.com/paper/2405.16884/) (2024)
- [Entity Matching using Large Language Models](https://mlsanity.com/paper/2310.11244/) (2023)
- [Using ChatGPT for Entity Matching](https://mlsanity.com/paper/2305.03423/) (2023)
- [WDC Products: A Multi-Dimensional Entity Matching Benchmark](https://mlsanity.com/paper/2301.09521/) (2023)
- [Fine-tuning Large Language Models for Entity Matching](https://mlsanity.com/paper/2409.08185/) (2024)
- [Cost-Effective In-Context Learning for Entity Resolution: A Design Space Exploration](https://mlsanity.com/paper/2312.03987/) (2023)
- [A Survey of Blocking and Filtering Techniques for Entity Resolution](https://mlsanity.com/paper/1905.06167/) (2019)
- [End-to-End Entity Resolution for Big Data: A Survey](https://mlsanity.com/paper/1905.06397/) (2019)
- [In-context Clustering-based Entity Resolution with Large Language Models: A Design Space Exploration](https://mlsanity.com/paper/2506.02509/) (2025)
- [BoostER: Leveraging Large Language Models for Enhancing Entity Resolution](https://mlsanity.com/paper/2403.06434/) (2024)
- [On Leveraging Large Language Models for Enhancing Entity Resolution: A Cost-efficient Approach](https://mlsanity.com/paper/2401.03426/) (2024)
- [AnyMatch -- Efficient Zero-Shot Entity Matching with a Small Language Model](https://mlsanity.com/paper/2409.04073/) (2024)
- [Bayesian Estimation of Bipartite Matchings for Record Linkage](https://mlsanity.com/paper/1601.06630/) (2016)
- [Heterogeneity in Entity Matching: A Survey and Experimental Analysis](https://mlsanity.com/paper/2508.08076/) (2025)
- [DistillER: Knowledge Distillation in Entity Resolution with Large Language Models](https://mlsanity.com/paper/2602.05452/) (2026)

## Abstract (arXiv)

We release OpenSanctions Pairs, the first large-scale public benchmark for entity matching on sanctions and OSINT data. The dataset includes 755,540 expert-labeled pairs over 1 million entities, aggregated from 293 source datasets across 45 jurisdictions. It captures real-world diversity in compliance data, spanning multiple languages and writing systems (e.g., Latin, Cyrillic, Arabic), inconsistent structure, and time-varying provenance, and is substantially more heterogeneous than prior entity matching benchmarks. As baselines, we evaluate the production rule-based matcher (nomenklatura RegressionV1) alongside open- and closed-source LLMs in both zero- and few-shot settings, each tested with and without MIPROv2 prompt optimization to control for prompt sensitivity. The rule-based baseline reaches 91.3% F1; GPT-4o achieves the best result at 99.0% F1, and a locally deployable open-source model (DeepSeek-R1-Distill-Qwen-14B) achieves 98.2% F1. The rule-based baseline and LLMs fail in complementary ways: rules over-match, while LLMs struggle with cross-script transliteration. These results suggest that pairwise matching performance is approaching a practical ceiling and shift attention toward pipeline components such as blocking, clustering, and uncertainty-aware review.