Wilson interval
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 Wilson interval means
A Wilson interval is a range around an observed proportion that stays inside [0,1] and stays sensible on a small sample. It exists because the textbook normal-approximation interval does neither: it breaks down when the proportion is near zero or one, or when the sample is small, producing intervals that extend beyond [0,1] and, at a zero-error observation, an interval of exactly zero width. Since 'we saw no errors' is the interesting case for a merge-safety system, that failure is not academic.
The Wilson interval solves it by inverting the score test rather than the Wald test. At an observed proportion of 1.0 it yields an asymmetric interval whose lower bound depends on the sample size — which is the behaviour a reader needs, because a perfect score on 40 trials and a perfect score on 400 are not the same evidence.
The practical discipline that follows is to publish the lower bound rather than the point estimate. A point estimate of 1.0 invites the reader to believe in perfection; a bound says what the data can actually support at 95%, and it moves as more evidence arrives.
Two figures on this site are Wilson bounds and are labelled as such. Automated merge precision on the gate of 2026-09-05 bounds at 98.94%: The 95% Wilson lower bound of 357 error-free automated merges. Proving a 99% lower bound needs at least 381 consecutive clean merges, so this is 24 short of it — and we publish the bound rather than the point estimate. And the calibrated confidence headline of 0.9968 is the nominal 95% wilson lower bound of 1,190 historical prompt-v8 uncorroborated match verdicts with zero recorded errors. The 2026-08-29 population pools six input shapes with repeated company families and calibration/evaluation overlap. Its independence and transfer to new customer inputs are unproven. Legacy replay cannot certify current calibration.
Source
- Wilson (1927), “Probable Inference, the Law of Succession, and Statistical Inference”, Journal of the American Statistical Association 22(158), 209–212
The origin of the score interval. The zero-error closed form quoted on this site, n / (n + z²), is that interval's lower bound at an observed proportion of 1; the arithmetic is ours.
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.
- How to measure entity resolution accuracyThere is no single accuracy number for an entity resolution system, and any vendor quoting one is quoting a property of their fixture rather than of their engine.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.
Calibration
A confidence score is calibrated when it matches observed reality — of the decisions returned at 0.95, about 95% should be correct — which is a different property from being accurate, and a much rarer 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.
Expected calibration error (ECE)
Expected calibration error summarises miscalibration as the average gap between stated confidence and observed accuracy across confidence buckets, weighted by how many predictions fall in each.
False bind
A false bind is Entity Bind's name for the error it treats as unacceptable: a record attached to the wrong company entity and returned as a decided match.
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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