Cluster metrics
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 cluster metrics means
Cluster metrics score the groups a resolver produced against the groups that are true. The family includes B-cubed, the variation of information, pairwise-counting cluster measures, and the older MUC and CEAF metrics from coreference research. What they share is the unit of evaluation: a predicted partition of records compared against a true partition, rather than a list of verdicts compared against a list of labels.
They exist because clustering is where the compounding happens. Chained merges create clusters far larger than any single decision implies, and missed edges split a family into pieces that no pairwise metric penalises. A resolver's true operational quality is a property of the partition it leaves behind, because the partition is what downstream systems consume.
One practical caution: cluster metrics are extremely sensitive to what is in the fixture. A fixture full of true singletons scores well on almost any metric, and a fixture built by pulling in every same-named sibling scores worse while being far more informative. Sibling-completeness is the property to ask about before comparing two cluster numbers.
Entity Bind's representative fixture is built sibling-complete for exactly this reason: 317 live GB companies sampled from the 2026-08-01 Companies House snapshot, with same-name siblings included. Identifier-rich arrivals seed the catalogue before the weaker input shapes are evaluated. Cluster figures from that instrument and from the continuous-integration gate are published together and never averaged.
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.
B-cubed (B³)
B-cubed is a cluster-level evaluation metric that scores precision and recall from the point of view of each individual record, asking what share of its assigned cluster genuinely belongs with it and what share of its true family it was placed with.
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.
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.
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.
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.
Start with your own company records.
Create your workspace, verify your email and run your first company file. The Free plan needs no card. Create API keys in your workspace when you need an integration.
300 credits. No card.
For a guided review of your file, explore the Merge Safety Sprint →