Learn entity resolution well enough to defend a merge

It is working out which records refer to the same real-world thing, and joining them under one identifier. If your CRM has "Acme Ltd", "ACME Limited" and "Acme Holdings Ltd", entity resolution decides which of those are the same company and which is a different legal entity that happens to share a name stem. The output is a set of clusters with stable identifiers, not a list of similarity scores.

They solve the same problem and come from different traditions. Record linkage comes from statistics and public health, usually means joining two defined files, and is measured over pairs. Entity resolution comes from the database community, assumes multiple or continuously arriving sources, produces a persistent partition with stable identifiers, and is measured over clusters. If you have two files, say linkage. If you need canonical entities, say resolution.

Because the population of possible decisions is every pair of records and almost all of them are non-matches. A system that says "not a match" to everything scores above 99.99% accuracy on a ten-thousand-record file while resolving nothing. Accuracy is not a slightly weak metric here, it is a disqualified one. What you want instead is precision on automated decisions, the automation rate it was drawn from, cluster metrics, an interval, and the input shapes it was measured over.

It can be, and the defence is publishing both numbers. A system that abstains on everything hard shows high precision and a low automation rate, and the two figures together make that visible immediately. Perfect precision at zero automation is trivial, which is why neither number is ever published here without the other. What abstention is legitimately for is the population where the evidence genuinely does not discriminate — a truncated name whose missing tokens were the ones that mattered cannot be resolved by a better model.

It means the score is a frequency you can act on: among all the answers returned at 0.90, about 90% are correct. Calibration is a property of a population rather than of an individual answer, so a calibrated confidence has to name the population it was derived from and its size. A score with no stated population cannot be checked and should not be thresholded against.

Within its own register, yes. Within a country, usually not, and that is the assumption that produces wrong merges. A company number plus a country is not a unique key and never was: 1,071 registers serve 232 jurisdictions, 220 of those jurisdictions run more than one, German numbers are qualified by the court that holds them, and Poland runs three parallel number spaces. Treating the pair as unique produces false positives, which is why the qualifier is carried on the record. The fix is to carry the issuing authority alongside the number rather than to infer it from a country column.

Legal Entity Identifier. It is a twenty-character alphanumeric code defined by ISO 17442 that identifies a legal entity participating in a financial transaction. Global: roughly 3.4 million LEIs assigned across all registration statuses, about 2.0 million of them with registration status ISSUED and most of the rest LAPSED (GLEIF, golden copy of 2026-09-25), issued by GLEIF, which issues them through accredited Local Operating Units, and the reference dataset is published openly.