B-cubed (B³)
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 B-cubed (B³) means
For each record, B-cubed precision is the proportion of the records in its predicted cluster that are truly the same entity, and B-cubed recall is the proportion of the records in its true cluster that were actually placed with it. The overall scores are the averages of those per-record values, and B-cubed F1 is their harmonic mean.
The averaging is what makes it useful. Because every record contributes equally, a single catastrophic merge of two large clusters is penalised in proportion to how many records it damaged, rather than counting as one bad decision. It also behaves sensibly on singletons, which matters in entity resolution where most records are genuinely alone in their cluster.
B-cubed is the metric to ask a vendor for when they quote pairwise numbers. Pairwise precision can stay high while cluster quality collapses, because a handful of transitively chained bad edges produce enormous wrong clusters from very few wrong pairs.
On the 317-company live-adjudicated recording of 2026-08-29, Entity Bind measured B-cubed cluster precision of 1.0000. Cluster F1 0.9646 on the same 317-company representative recording of 2026-08-29. Pairwise numbers are never published without these. The continuous-integration gate's own cluster F1 is 0.7584, and the two are reported side by side rather than blended: 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.
Source
- 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.
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.
Cluster metrics
Cluster metrics evaluate the groups a resolver produced rather than the pairs it judged, which is the only way to see errors that only appear once pairwise decisions are chained into entities.
Pairwise metrics
Pairwise metrics evaluate a resolver one candidate pair at a time — did it correctly decide that these two records are or are not the same entity — and they systematically flatter systems whose errors chain together.
Cluster
A cluster is the set of records a resolver has decided belong to one entity, and the object that cluster-level metrics are scored against.
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.
Fragmentation
Fragmentation is the opposite failure to over-merging: one real entity split across several clusters, so the same company is counted, contracted with or invoiced more than once.
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.
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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