Transliteration
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 matching methods.
How two records are compared and how the comparison is made cheap enough to run. Blocking, deterministic rules, probabilistic scoring, string distance.
every figure, with its population →What transliteration means
Transliteration is the mapping that rewrites a name from one writing system into another, so two records kept in different alphabets can be compared. A company registered in Athens, filed in Greek, may appear in a supplier master in Latin script, in three different romanisations, none of which matches the register's own. It is not translation: it converts characters and sounds rather than meaning, so 'Οδός' becomes 'Odos' rather than 'Street'.
The difficulty is that transliteration is many-to-many. There are several competing standards per script — ISO, BGN/PCGN, national systems — and real data uses all of them plus improvisation. A robust pipeline therefore generates several romanisations of a source name rather than picking one, indexes all of them for retrieval, and treats an agreement in any of them as weaker evidence than an agreement in the original script.
Two related operations often get conflated with it. Unicode normalisation, which makes the same visible character compare equal regardless of how it was composed, is a prerequisite and not optional. Confusable folding, which maps a Cyrillic 'а' onto a Latin 'a', prevents a homoglyph from silently splitting an entity — and is also the mechanism a bad actor would use to hide one.
Entity Bind performs transliteration in the normalisation stage. Its measured name-tier accuracy has been recorded on Latin-script registers only, and the region parity work explicitly does not carry the live-adjudicated name tiers across jurisdictions.
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.
- Register coverageThe registers the resolver reads, the licence each one publishes under, and how often a live entity's name is unique once it is tidied to a common form.Open
- How it worksThe seven-stage cascade in order, what each stage can settle on its own, and what it hands to the next.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.
Normalisation
Normalisation rewrites a record into a comparable form — case, punctuation, legal forms, identifier shapes — before any comparison is attempted, and it is where most of the achievable accuracy in entity resolution is won or lost.
Fuzzy matching
Fuzzy matching compares strings by similarity rather than equality, returning a score between zero and one — a useful retrieval signal and, on its own, not a calibrated basis for a merge.
Blocking
Blocking is the technique that makes entity resolution affordable by comparing each record only against a small set of plausible candidates instead of against every other record.
Edit distance
Edit distance counts the minimum number of single-character insertions, deletions or substitutions needed to turn one string into another; Levenshtein distance is the standard form.
The other 12 terms on this shelf
How two records are compared and how the comparison is made cheap enough to run. Blocking, deterministic rules, probabilistic scoring, string distance.
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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