Fragmentation
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 fragmentation means
Fragmentation is one real company left sitting in two or more clusters, and it is what a conservative resolver produces when it is wrong. It shows up as duplicate accounts in a CRM, duplicate vendors in a supplier master, and an entity count that overstates the customer base. Its costs are real but bounded and detectable: someone eventually notices two records with the same address.
Because it is the safer failure, systems built for merge safety deliberately accept more of it. Every abstention is a fragmentation the system chose rather than risk a wrong binding, and every unresolved record is a predicted singleton as far as a cluster metric is concerned. That is not free, so the cluster metric that penalises fragmentation is published alongside the precision metric that does not.
Entity Bind does exactly that, and the effect is visible in the gap between its two instruments. The continuous-integration gate's cluster F1 is 0.7584: 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.
Fragmentation also has a register-shaped cause that no engine can fix. Where a large share of companies share a name, a file without a discriminating column will fragment: on SIRENE, A third of French companies share their exact name — 1,003 live companies sit on one name. A French file needs a town or postcode column, and with one it measured 2,998 of 3,000 on the postcode tier.
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
- Master data managementGolden records, deciding which value wins when two records merge, and what a durable entity id has to outlive.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 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.
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.
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.
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.
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.
SIREN and SIRET
SIREN is the nine-digit identifier France assigns to a legal unit and SIRET is the fourteen-digit identifier of one of its establishments — the same company at two different levels of granularity.
Deduplication
Deduplication is entity resolution applied within a single dataset: finding the rows that are the same thing and deciding which of them may safely be collapsed.
- GuideHow to deduplicate a supplier masterDuplicate vendors, payment leakage, remittance conflicts and parent concentration. The accounts payable playbook for finding which vendor records are one company, with a citation for every finding.
- Use caseSupplier master and accounts payableDuplicate vendors, remittance conflicts and concentration you cannot see.
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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