entity-resolution

Person name matching to existing slugs prevents duplicate records by comparing normalized names against existing data using family name, initials, birth year, and contextual role alignment to find reliable matches. It eliminates inconsistent person entries in government datasets.

Deduplicating person records from appointment announcements involves cross-referencing named individuals against existing stable person slugs, applying weighted matching based on family name and initials, and generating new standardized slugs only when no existing match is found.

Entity resolution handles ambiguous matches by enforcing confidence thresholds and flagging low-confidence results or multiple high-scoring candidates for human review, preventing incorrect record linking when family names and initials are too similar to distinguish automatically.

Yes, person matching for government datasets with Dutch tussenvoegsels and honorifics works by normalizing names first—stripping honorifics and handling tussenvoegsels—before comparing them against existing records using weighted criteria like family name, initials, and birth year.

You need to generate new person slugs during data deduplication when no existing match is found in the dataset, creating consistent `person:*` slugs following project slugify conventions to maintain dataset integrity without forcing incorrect links to unrelated records.

Person matching requires contextual role and organization alignment because family name and initials alone are often insufficient, so weighting role and organization details alongside birth year ensures the matched identifier truly corresponds to the correct individual in the government dataset.