Entity matching: how to tell if two company records are the same, a subsidiary, or unrelated

Entity matching, or entity resolution, determines whether records refer to the same real-world entity through stages such as standardization, blocking, similarity comparison, scoring, and clustering. Traditional approaches—including deterministic identifiers, string similarity, probabilistic models such as Fellegi-Sunter, and supervised learning—handle many cases but can fail when company relationships depend on external facts, including renames, acquisitions, subsidiaries, spin-offs, and name collisions. The text proposes using a web-grounded decision step for ambiguous pairs that classifies records as the same entity, parent or subsidiary, unrelated, or unknown, with citations supporting the result. Examples involving Meta and Facebook, Slack and Salesforce, Siemens entities, Delta companies, Mondelēz and Kraft Heinz, and similarly named Acme firms illustrate why relationship-aware decisions may be more useful than a binary match outcome. It recommends reserving this higher-cost, slower step for uncertain cases after conventional matching has accepted or rejected clear pairs, sending low-confidence results for human review and using reviewed labels to improve the underlying matcher.

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