Recognises an enterprise entity with no glossary term
A name with no definition behind it; two readers will mean two things.
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 | 56% | Not shown |
| gemini-3.5-flash-lite | Low cost | 52% | Not shown |
| gpt-6-luna | Low cost | 57% | Not shown |
| claude-haiku-5-5 | Low cost | 56% | 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: The entity and its term
How it is assessed: Flags the deficient case and passes the acceptable one. Deficient: EN-010 Chargeback, term: null Acceptable: term: BT-115
Also known as: Entity with no glossary term
Related knobits
Builds on
All 17 knobits of Build the enterprise data model of subject areas.
Source: Review checklist 02-enterprise-data-model, row 10 (DMBOK)