Jaro–Winkler
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 Jaro–Winkler means
The Jaro similarity counts matching characters that fall within a distance window of each other and penalises transpositions, producing a score in [0,1]. Winkler's modification raises the score for strings sharing an initial prefix of up to four characters, on the empirical observation that human errors in names cluster later in the string than at the beginning.
That prefix boost is why the measure is popular for personal and company names and why it must be used carefully in company data. Company names share prefixes structurally rather than accidentally: 'Aviva Investors', 'Aviva Life' and 'Aviva Insurance Services' are three different legal persons that Jaro–Winkler will score as highly similar. The same boost also makes a truncated name look like a strong match for whichever sibling shares its opening tokens.
It is a good retrieval and blocking signal and a weak deciding signal. It has no notion of tokens, of token rarity, or of the discriminating power of the part of the name that was cut off.
That last failure is measurable. On the 317-company representative fixture of 2026-08-29, records whose names had been truncated by a field-length limit resolved at 58.10% (61/105) — 44 of 105 come back as an abstention with a shortlist, which is the correct answer for a name whose missing tokens were the discriminating ones.
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.
- Compared with fuzzy matchingA score for how alike two names look, against a decision about which company they name — and the rows where the fuzzy matching you already run wins.Open
- Blocking and candidate generationComparing every record against every other record is quadratic and therefore impossible at any real scale. That stage is blocking, also called indexing or candidate…Open
- How it worksThe seven-stage cascade in order, what each stage can settle on its own, and what it hands to the next.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.
Edit distance
Edit distance counts the minimum number of single-character insertions, deletions or substitutions needed to turn one string into another; Levenshtein distance is the standard form.
Fuzzy matching
Fuzzy matching compares strings by similarity rather than equality, returning a score between zero and one — a useful retrieval signal and, on its own, not a calibrated basis for a merge.
Abstention
An abstention is a first-class 'we do not know' returned instead of a match: the record comes back unresolved, with a ranked shortlist, a typed reason and the column that would have decided it.
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.
Normalisation
Normalisation rewrites a record into a comparable form — case, punctuation, legal forms, identifier shapes — before any comparison is attempted, and it is where most of the achievable accuracy in entity resolution is won or lost.
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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