Recognises no discriminator for same-name relatives

R-308 · Skill knobit · Activity: Define the matching rules · Competence: Reference and master data

Nothing separates people who share name and address.

Can AI do this?

Not settled yet The benchmark tested this, but the models' answers do not settle yet whether they master it. 0 of 4 models in the current benchmark master it.

Model	Tier	Probability of mastery	Status
gemini-4-argon	Flagship	88%	Not shown
gemini-3.5-flash-lite	Low cost	25%	Not shown
gpt-6-luna	Low cost	88%	Not shown
claude-haiku-5-5	Low cost	89%	Not shown

Mastered means a probability of at least 90% that the model has this knobit, estimated from its graded answers. “Not shown” means the answers do not settle it either way. How the models are tested.

What it looks like in practice

Expected result: A discriminating field such as date of birth

How it is assessed: Flags the deficient case and passes the acceptable one. Deficient: No rule compares date of birth or generation suffix Acceptable: Date of birth as a required agreeing field

Also known as: No discriminator for same-name relatives

Related knobits

Builds on

R-351 Explains probabilistic record linkage

Usually followed by

R-331 Requires a discriminator before merging same-name relatives

All 16 knobits of Define the matching rules.

Source: Review checklist 03-matching, row 8 (FS; DMBOK)