Explains essential and delimiting characteristics
The characteristics every instance has and the ones telling the concept from its siblings.
Can AI do this?
AI does this well Most of the tested AI models master this. 3 of 4 models in the current benchmark master it.
| Model | Tier | Probability of mastery | Status |
|---|---|---|---|
| gemini-4-argon | Flagship | 100% | Mastered |
| gemini-3.1-pro-preview* | Flagship | 100% | Mastered |
| gpt-6-astra* | Flagship | 100% | Mastered |
| claude-fable-5-1* | Flagship | 100% | Mastered |
| gemini-3.8-flash* | Mid tier | 100% | Mastered |
| gpt-6.1-sol* | Mid tier | 100% | Mastered |
| claude-sonnet-5-5* | Mid tier | 100% | Mastered |
| gemini-3.5-flash-lite | Low cost | 94% | Not shown |
| gpt-6-luna | Low cost | 100% | Mastered |
| claude-haiku-5-5 | Low cost | 99% | Mastered |
* From the earlier benchmark of 2026-10: 10 data-modelling test tasks, 3 runs per task, graded by claude-opus-5-5. These models have not taken the current 123 tasks yet; the two methods differ, so compare their figures with care. They are left out of the “most models” counts and the best-model picks.
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 characteristics every instance has and the ones telling the concept from its siblings
How it is assessed: Decides for sample definitions whether they are too broad or too narrow
Related knobits
Needed for
- M-303 Recognises a negative definition
- M-304 Recognises a definition too broad for its concept
- M-305 Recognises a definition too narrow for its concept
- M-313 Writes an intensional definition of a business term
All 18 knobits of Define business terms in the glossary.
Source: Data-management practice used by the stage 3 reference and review checklist; formerly data-modelling K-256