Record linkage
The definition as the field writes it comes first, weaknesses and all. What Entity Bind does about it comes last, in its own paragraph, so you can take one without the other. Filed under foundations.
What the problem is called, and what the objects in it are. Entity, record, cluster, and the four industry names for the same job.
every figure, with its population →What record linkage means
Record linkage is the older, more statistical name for the same problem entity resolution addresses. It comes out of public health and official statistics — Halbert Dunn's 1946 paper on assembling a person's 'book of life' from separate registrations is the usual origin cited — and it carries the framing of that world: two files, a set of comparison fields, and a decision rule with quantified error rates.
The canonical formulation is Fellegi and Sunter's, published in 1969, which treats each candidate pair as a draw from either the matched or the unmatched population and accumulates evidence field by field. That framing is still the right mental model for probabilistic matching today, and the vocabulary the field uses — match weight, m-probability, u-probability, the clerical review region — is theirs.
In practice the difference between 'record linkage' and 'entity resolution' is emphasis rather than substance. Record linkage tends to assume a fixed pair of files and to care about the statistical properties of the linkage; entity resolution tends to assume a running system with a persistent store of entities that new records arrive against, and to care about clustering, survivorship and correction over time.
Entity Bind is a record linkage service in the classical sense and an entity resolution service in the operational one. Its scorer accumulates evidence field by field and splits the result three ways — accept, reject, and a band it will not decide — which is Fellegi–Sunter in shape. What it writes to is a durable catalogue of company entities that later submissions attach to or are refused against.
Source
- Fellegi and Sunter (1969), “A Theory for Record Linkage”, Journal of the American Statistical Association 64(328), 1183–1210
The paper behind the three-region rule and the m and u probabilities. Its optimality result holds under the model's assumptions, conditional independence between fields among them, which real company data usually breaks.
Where this shows up in Entity Bind
Three pages where this term stops being vocabulary and starts changing an answer, a payload or a price.
- Entity resolution vs record linkage: which term is rightRecord linkage and entity resolution describe the same underlying task — deciding which records refer to the same real-world entity.Open
- The accuracy methodEvery published rate with the population it was measured over, its interval and its fixture — including the input shapes that fail.Open
- Compared with SplinkThe open-source library most teams reach for first, which learns how much each agreeing field is worth — the Fellegi–Sunter model — and what it does better than we do.Open
The fastest way past a definition is a file: which of your rows are the same company, which of your existing matches we would not have made, and what we could not decide.
Start freeThe terms next to this one
Each with its own sentence, because the next question is usually about the word beside this one.
Entity resolution
Entity resolution is the process of deciding which records, across one or many datasets, refer to the same real-world thing — and, when the evidence does not settle it, deciding that too.
Fellegi–Sunter
Fellegi–Sunter is the 1969 statistical framework for record linkage that scores a candidate pair as a log-likelihood ratio of field agreements and splits the result into three regions: accept, reject, and refer for review.
Deduplication
Deduplication is entity resolution applied within a single dataset: finding the rows that are the same thing and deciding which of them may safely be collapsed.
Probabilistic matching
Probabilistic matching scores a candidate pair by weighing the agreement and disagreement of several fields into a single number, then applies thresholds to that number rather than to any one field.
Cluster
A cluster is the set of records a resolver has decided belong to one entity, and the object that cluster-level metrics are scored against.
The other 9 terms on this shelf
What the problem is called, and what the objects in it are. Entity, record, cluster, and the four industry names for the same job.
Internal fixtures only. The historical 2026-08-29 per-tier recording covers 317 GB companies with identifier-rich arrivals seeding the catalogue first; the context tier includes country and registered-town prose. Its legacy replay has no full-prompt-verified calls and cannot certify current calibration. The September 5 CI reports cover 1,176 labelled pairs and 705 records over 250 companies using offline stubs. These are not independent customer holdouts or live production validation; the 99% minimum-input product target remains unmet. Scope is company and organisation identity only — not people, vessels, bank-account ownership, sanctions or emissions. Results may not be used for eligibility decisions about individuals, including employment, credit and tenancy.
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