Entity resolution vs record linkage: which term is right
For the team that owns a company master and has to defend it.
A stewardship queue, a CRM after a migration or a merger, a supplier file before a payment run, counterparty onboarding that has to survive an audit. Read the argument here, take the takeaways at the foot into your next vendor conversation, and send us a file when you want the same measurement on your own rows.
Foundations · 1,579 words · every figure carries the population it was measured over
8 min read, last revised . 4 questions answered at the foot.
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The short answer
If you have two files and you want to join them, say record linkage. If you have one or many sources and you want a canonical set of entities with stable identifiers, say entity resolution. If you have one file and you want to collapse duplicates within it, say deduplication. All three are the same decision problem wearing different job descriptions, and the difference in framing is not cosmetic: it changes the denominator you naturally reach for when you measure.
Almost every argument about these words is really an argument about scope. The linkage framing assumes two populations and a one-to-one correspondence between them, which makes precision and recall over candidate pairs the obvious metrics. The resolution framing assumes an open-ended set of records arriving over time, no guarantee of one-to-one anything, and a persistent entity store, which makes cluster quality and identifier stability the obvious metrics. A system evaluated only in the first framing can look excellent and still produce bad entities.
Where record linkage came from
Record linkage is the older term and the more rigorous tradition. Halbert Newcombe and colleagues set out the probabilistic approach in Science in 1959, working on vital records — joining birth records to marriage records to study heredity. Ivan Fellegi and Alan Sunter formalised it in 1969 in the Journal of the American Statistical Association, giving the field a decision-theoretic framework with an optimality result, which is still the reference model most matchers are a variation on.
The tradition's centre of gravity is official statistics, census work and health research: two administrative files, a population you can characterise, and a requirement that the linkage error be quantified because downstream inference depends on it. That is why the linkage literature is unusually careful about how often a field agrees when two records are the same person and how often it agrees when they are not — the m and u probabilities — about estimating both without labelled data, and about the effect of linkage error on the analysis that follows. It is also why the default unit is the pair.
The term is still the right one in its home context. If you are linking a hospital admissions extract to a mortality register, you are doing record linkage, the literature you want is the linkage literature, and calling it entity resolution will make it harder rather than easier to find the relevant prior work.
Where entity resolution came from
Entity resolution is the database community's name for the general problem, and it became the dominant term as the setting shifted from two-file linkage to messy, multi-source, continuously arriving data. The framing is more general in three specific ways, and each generalisation is where a linkage-shaped evaluation stops being sufficient.
It drops the two-file assumption, so records may arrive from any number of sources and from the same source repeatedly. It drops the one-to-one assumption, so an entity may have any number of records and a record may be the first evidence of an entity nobody has seen before — which means a resolver has to be able to mint a new entity, a decision the linkage framing has no vocabulary for. And it makes the output a persistent partition rather than a set of accepted pairs, which introduces problems the pair framing does not have: transitive closure, order dependence, and what happens when a later record contradicts an earlier merge.
Order dependence in particular is invisible in a pairwise evaluation and lethal in production. A sequential loop over a file can produce different final clusters for different input orders, and almost nobody checks. We measured an earlier implementation of our own producing nine different partitions across the twenty-four orderings of four records, which is why the current one clusters by connected components over a frozen snapshot rather than folding each record into whatever it happens to meet first.
The rest of the vocabulary, and who owns each word
Six other terms circulate, and they are not synonyms even where they overlap. Getting them wrong wastes a procurement cycle, because a vendor selling identity resolution and a vendor selling entity resolution are frequently selling to different regulations.
| Term | Comes from | Unit of work | Use it when |
|---|---|---|---|
| Record linkage | Statistics, public health, official statistics | A pair of records across two files | You are joining two defined populations and need to quantify linkage error for downstream inference. |
| Entity resolution | Databases and computer science | A cluster of records over an entity | You have multiple or continuous sources and need canonical entities with stable identifiers. |
| Deduplication | Data quality and MDM practice | Records within one file | The scope is a single source and the output is that source with duplicates collapsed. |
| Entity matching | The machine learning benchmark literature | A labelled pair, match or not | You are talking about the pairwise classification task in isolation, usually to benchmark a model. |
| Entity linking | Natural language processing | A mention in text, mapped to a knowledge base | The input is free text and the task is grounding a mention to a known entity. Different problem, adjacent name. |
| Identity resolution | Marketing and adtech | A person across devices and channels | The subject is an individual. It carries a different regulatory surface entirely, and conflating it with company resolution is a compliance error rather than a wording one. |
| Master data management | Enterprise architecture | A governed golden record and its lifecycle | The subject is the organisational programme — stewardship, governance, workflow — of which resolution is one component. |
Entity Bind resolves company and organisation identity only. It does not resolve people, and identity resolution in the adtech sense is not something it does or should be used for.
Why the word changes the measurement
Ask a linkage-framed system how accurate it is and you will get precision and recall over pairs. Ask a resolution-framed system and you should get cluster metrics. This is where the vocabulary stops being a matter of taste, because the two can differ by a very large margin over the same run, and the direction of the difference is not random: pairwise is almost always the kinder number.
Our own figures show the size of the gap. Charge each error to every record in the cluster it landed in — the B-cubed convention — and on the live-adjudicated 317-company fixture of 2026-08-29, cluster precision is 1.0000 and cluster F1 is 0.9646. On the continuous-integration gate of 2026-09-05 — a different instrument over a different fixture, never blended with the first — pairwise precision is 1.0000 on 357/357 merges while cluster F1 is 0.7584. Those are not contradictory measurements of one thing. They are two different questions, and a run is only legible with both reported.
The gate's cluster F1 sits so much lower for a reason worth following through: an unresolved record counts as a predicted cluster of one, and the gate's name-only records abstain by construction, so every family containing one gets split. B-cubed cluster F1 on the gate's own 705-record fixture, held as a regression ratchet against a 0.75 floor rather than offered as a headline. It reads far below the live recording's 0.9646 because an unresolved record is a predicted singleton and all 205 name-only arrivals on this fixture abstain by construction, splitting the families they belong to. The two cluster figures come from different instruments over different fixtures and are never averaged.
Which term to use with which audience
In a procurement document, name the task rather than the tradition: "resolve supplier records to canonical legal entities, with an identifier that persists across loads" is unambiguous where "do entity resolution" is not. In a literature search, use the term the field uses — Fellegi-Sunter, m and u probabilities, and linkage error live under record linkage; blocking, canonicalisation and cluster metrics live under entity resolution.
In a vendor conversation, the term someone reaches for tells you what they will measure by default, which tells you what to ask for explicitly. A supplier who talks in pairs will quote you pairwise precision unless you ask for B-cubed. A supplier who talks in golden records may not have measured the matching separately from the survivorship at all, and those two failures have completely different fixes.
What to take away
- Record linkage assumes two files and measures pairs; entity resolution assumes many or continuous sources and measures clusters. The task is the same, the defaults are not.
- Entity resolution additionally has to handle minting new entities, transitive closure and order independence — none of which a pairwise evaluation can see.
- Entity linking (text to knowledge base) and identity resolution (people across devices) are adjacent names for genuinely different problems, and conflating the latter with company resolution is a regulatory error.
- Pairwise metrics are systematically kinder than cluster metrics over the same run, so the vocabulary a supplier uses predicts which number you will be shown.
Those takeaways hold for anybody's data. The number for yours comes out of your own rows — a few hundred of them, with the matches you already have beside them.
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Where this article is applied
1 pages name this method as the working behind what they claim.
Is this your file?
The article is for anyone. The measurement behind it is worth commissioning for one kind of team.
Teams with a company master they have to defend — an MDM or stewardship queue, a CRM after a migration or a merger, a supplier file before a payment run, or counterparty onboarding that has to survive an audit.
Buying breadth as the product: we hold a fraction of an incumbent’s records and are not building a global company graph. Entity Bind resolves companies and organisations only. It does not resolve people, vessels, bank-account ownership, sanctions exposure, emissions or geolocation, and its results may not be used for eligibility decisions about individuals, including employment, credit and tenancy.
Sources
The papers, regulators and registers this article quotes, each with the caveat that travels with it.
- Newcombe, Kennedy, Axford and James (1959), “Automatic Linkage of Vital Records”, Science 130(3381), 954–959
The probabilistic approach before it was formalised, worked out on birth and marriage records. It describes linking records about people, which is outside this product's scope; it is cited for the method.
- 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.
- Bagga and Baldwin (1998), “Entity-Based Cross-Document Coreferencing Using the Vector Space Model”, COLING-ACL 1998
Section 7.3 sets out the B-CUBED scoring algorithm, written for cross-document coreference of people's names rather than for company records. The per-record averaging is the part this site borrows.
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.
Figures read off the product repository on 2026-09-05. The per-tier rates come from the live-adjudicated 317-company recording of 2026-08-29; the gate figures come from the stub-adjudicated continuous-integration run and are a floor rather than a ceiling. The two are never averaged. Entity Bind holds no SOC 2, no ISO 27001 and no other certification, and claims none. Every figure, with the population it was measured over and the method behind both recordings, is set out on the accuracy page. The retention, erasure, tenancy and sub-processor detail a security review asks for is under Trust and security, with where assurance stands: no external validation of the accuracy figures and no customer holdout.
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The questions this article answers
Is record linkage the same as entity resolution?
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.
What is the difference between entity resolution and entity linking?
Entity linking is a natural language processing task: take a mention in free text and map it to an entry in a knowledge base. Entity resolution takes structured records and decides which of them describe the same real-world entity. They share a word and very little else, though a system that accepts free-text context alongside a record is doing a little of both.
Is identity resolution the same thing for people?
Roughly, and the difference matters more than the similarity. Identity resolution in the adtech sense joins a person across devices, cookies and channels, and it sits under a completely different regulatory surface. Entity Bind resolves company and organisation identity only — not people, and not eligibility decisions about individuals — so identity resolution is not a use case it supports.
Should our RFP say entity resolution or record linkage?
Say neither, and describe the output. "Resolve inbound supplier records to canonical UK and EU legal entities, return a stable identifier that persists across loads, and report an abstention rate per input shape" tells every bidder what is being bought. The category names each imply a default set of metrics, and stating the metric you want removes the ambiguity that the category name introduces.