Recall
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 measurement.
How you tell whether any of it worked, and which numbers may not be quoted without which other numbers.
every figure, with its population →What recall means
Recall is true positives divided by all the true matches that existed. A resolver that finds sixty of the eighty genuine duplicate pairs in a file has recall of 0.75, whatever its precision. In deduplication, missed recall means duplicate spend, duplicate outreach and a customer count that is wrong in a direction nobody notices.
Recall and precision trade off, and the mechanism of the trade is the threshold. Lowering the bar catches more true pairs and admits more false ones. Any system that improves both at once has either improved the evidence available to it — more sources, better normalisation, better retrieval — or moved to a different fixture.
Recall is harder to measure than precision, because it needs a labelled population in which the true matches are known exhaustively. Most reported recall figures are recall against a fixture, and the fixture's construction determines the number: a sibling-complete fixture that deliberately includes every same-named rival will report lower recall than a sample that quietly excluded the hard cases.
Entity Bind spends recall to protect precision: a record whose evidence will not clear the confidence floor comes back unresolved rather than bound. The resolution rates published per input shape are the coverage that policy leaves, and they sit beside the safety figures on every surface that carries either.
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.
- Precision, recall and abstentionThe precision-recall trade-off is usually described as a two-way choice, and in entity resolution that description is wrong. There is a third output — declining…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 audit packThe fixtures, the verdict logs and the replay path: what an evaluator needs to re-run a published rate for themselves.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.
Precision
Precision is the share of the matches a system asserted that were correct — and it is meaningless without the share of records the system was willing to decide at all.
F1 score
F1 is the harmonic mean of precision and recall, reported as a single number that is only interpretable once you know which of its two components is doing the work.
Recall@k
Recall@k is the share of records for which the correct entity appears anywhere in the top k candidates returned by retrieval, and it is the ceiling on everything a matcher downstream can get right.
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.
Confidence floor
A confidence floor is the score below which a system refuses to return a match at all, expressed as an acceptance contract rather than as a promise about accuracy.
Candidate generation
Candidate generation is the retrieval step that proposes the small set of entities a record might be, and its recall is the hard ceiling on everything the rest of the pipeline can achieve.
The other 13 terms on this shelf
How you tell whether any of it worked, and which numbers may not be quoted without which other numbers.
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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