u-probability
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 matching methods.
How two records are compared and how the comparison is made cheap enough to run. Blocking, deterministic rules, probabilistic scoring, string distance.
every figure, with its population →What u-probability means
Where m measures reliability, u measures coincidence. If two randomly chosen unrelated companies share a country 40% of the time, the u-probability of country agreement is 0.40 and country agreement is almost worthless as evidence. If two unrelated companies share an exact company name effectively never, the u-probability is tiny and agreement is nearly decisive.
u is usually estimated by sampling random pairs, which is cheap, or derived analytically from value frequencies. The refinement that matters most in company data is term-frequency adjustment: u should be computed per value rather than per field, because agreeing on 'Smith Consulting Limited' and agreeing on 'Vodafone Group Plc' are not the same event. Ignoring this is the single most common cause of over-merging in name-heavy files.
Register shape sets u directly, which is why Entity Bind measures name uniqueness per register before quoting anything about name matching. On the Companies House live universe of 5,464,811 live companies, 99.53% of names are unique; on SIRENE, 67.96% are — A third of French companies share their exact name — 1,003 live companies sit on one name. A French file needs a town or postcode column, and with one it measured 2,998 of 3,000 on the postcode tier.
Entity Bind implements the underlying idea as rarity weighting in retrieval and as fitted feature weights in scoring, rather than as an explicit u parameter per field.
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.
- Fellegi-Sunter explained, with worked arithmeticThe Fellegi-Sunter model, published in 1969, is the framework almost every probabilistic matcher is a variation on. Its idea is simple and still correct: for each field…Open
- Register coverageThe registers the resolver reads, the licence each one publishes under, and how often a live entity's name is unique once it is tidied to a common form.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
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.
m-probability
The m-probability of a comparison field is the probability that the field agrees given that the pair really is a match — in effect, one minus the rate at which that field is wrong, missing or mistyped in true matches.
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.
Match weight
A match weight is the log-scaled contribution one comparison makes to a pair's score, positive when agreement is evidence of a match and negative when disagreement is evidence against.
Blocking
Blocking is the technique that makes entity resolution affordable by comparing each record only against a small set of plausible candidates instead of against every other record.
Over-merge
An over-merge is a cluster containing records from more than one real entity — the failure that turns two customers, two suppliers or two counterparties into one.
SIREN and SIRET
SIREN is the nine-digit identifier France assigns to a legal unit and SIRET is the fourteen-digit identifier of one of its establishments — the same company at two different levels of granularity.
The other 12 terms on this shelf
How two records are compared and how the comparison is made cheap enough to run. Blocking, deterministic rules, probabilistic scoring, string distance.
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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