Corporate-Family Resolution Is Not a String-Matching Problem: A Public Benchmark Stratified by Name Visibility
Not on the map. Why?
Abstract (arXiv)
Deciding whether two supplier records belong to the same corporate family is a prerequisite for spend consolidation, credit exposure aggregation and sanctions screening. It is usually treated as entity matching, but the tasks differ: a family link connects records that are deliberately different entities, and the evidence often appears in neither record. We introduce CorpFam, a public benchmark of 54,864 candidate pairs over 10,307 corporate families, derived from 6,638,350 US federal award records in which every supplier self-reports its ultimate parent to a government registry. Pairs are stratified by name visibility: whether the names are identical after normalisation, share a distinctive token, or share none. Because strata have positive rates from 10.2% to 97.3%, we report per-stratum recall, base-rate invariant, rather than F1, which is not. The strongest of 5 matchers recovers 100.0% of identical pairs and 4.2% of invisible ones; no method exceeds 4.7% on the latter. The failure begins before matching. Blocking decides which pairs a matcher sees, and we evaluate 7 schemes spanning phonetic keys, attribute keys that ignore the name, and semantic nearest neighbours. None reaches three percent on invisible pairs, and their union recovers 6.8%. 93.2% of these links never enter the candidate set, so no matching-stage improvement can reach them. The links are real: against SEC Exhibit 21 subsidiary schedules, which share no provenance with procurement registration, 64.2% of invisible links are corroborated, against 0.16% under permuted parents and 0.41% against the same parent's wrong exhibit: two unrelated nulls agreeing to within 0.25 points. Corporate-family resolution is a retrieval problem misfiled as a matching problem; the intervention point is candidate generation, not ranking. The benchmark, adjudication log, and code reproducing every number are released.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 21 of its 50 references that are in the map. First posted on arXiv 2 Sep 2026.
Builds on
21 of 50 references.
References · 21
- 2025BrowseComp: A Simple Yet Challenging Benchmark for Browsing Agentscited by 647
- 2024Measuring short-form factuality in large language modelscited by 373
- 2025BrowseComp-Plus: A More Fair and Transparent Evaluation Benchmark of Deep-Research Agentcited by 200
- 2020Deep Entity Matching with Pre-Trained Language Modelscited by 144
- 2020Neural Networks for Entity Matching: A Surveycited by 33
- 2023Entity Matching using Large Language Modelscited by 31
- 2023Using ChatGPT for Entity Matchingcited by 22
- 2023WDC Products: A Multi-Dimensional Entity Matching Benchmarkcited by 17
- 2021Machamp: A Generalized Entity Matching Benchmarkcited by 16
- 2023Pre-trained Embeddings for Entity Resolution: An Experimental Analysis [Experiment, Analysis & Benchmark]cited by 13
- 2022Bridging the Gap between Reality and Ideality of Entity Matching: A Revisiting and Benchmark Re-Constructioncited by 10
- 2020Link Prediction using Graph Neural Networks for Master Data Managementcited by 8
9 more
- 2021Alaska: A Flexible Benchmark for Data Integration Taskscited by 7
- 2023CompanyKG: A Large-Scale Heterogeneous Graph for Company Similarity Quantificationcited by 6
- 2024Evaluating Blocking Biases in Entity Matchingcited by 3
- 2022How to reduce the search space of Entity Resolution: with Blocking or Nearest Neighbor search?cited by 2
- 2021Graph-based hierarchical record clustering for unsupervised entity resolutioncited by 1
- 2022CompanyName2Vec: Company Entity Matching Based on Job Adscited by 1
- 2023Transformer-based Entity Legal Form Classificationcited by 1
- 2024xLP: Explainable Link Prediction for Master Data Managementcited by 1
- 2026MaDI-Bench: An End-to-End Data Integration Benchmarkcited by 1
Built on by
Not cited yet.