Explains the rules for defining a concept
Explains how a concept is delimited by a definition - the nearest superordinate concept and the distinguishing characteristic; does not cover writing a definition.
Can AI do this?
AI does this well Most of the tested AI models master this. 4 of 4 models in the current benchmark master it.
| Model | Tier | Probability of mastery | Status |
|---|---|---|---|
| gemini-4-argon | Flagship | 100% | Mastered |
| gemini-3.5-flash-lite | Low cost | 100% | Mastered |
| gpt-6-luna | Low cost | 100% | Mastered |
| claude-haiku-5-5 | Low cost | 100% | Mastered |
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: An explanation of the rules of definition
How it is assessed: Names the parts of a definition and two common errors (too broad, circular definition)
Also known as: Selgitab mõiste defineerimise reegleid
Related knobits
Needed for
All knobits of Data modelling.
Source: Copy of k12 knobit K-11128 of the Estonian national curriculum register pk-valikained (product k12-curriculum, skills/knobits/k12/pk-valikained.yaml), Põhikooli RÕK, Teadusliku mõtlemise alused, III kooliaste, õpiväljund 11053; fingerprint translated from Estonian