Expresses a logical model as a UML class diagram
Classes, attributes, associations with multiplicities and generalisations equivalent to the model
Can AI do this?
People still lead Few or none of the tested AI models showed they master this. 0 of 4 models in the current benchmark master it.
| Model | Tier | Probability of mastery | Status |
|---|---|---|---|
| gemini-4-argon | Flagship | 81% | Not shown |
| gemini-3.5-flash-lite | Low cost | 80% | Not shown |
| gpt-6-luna | Low cost | 8% | Not shown |
| claude-haiku-5-5 | Low cost | 8% | 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: Classes, attributes, associations with multiplicities and generalisations equivalent to the model
How it is assessed: The loan and collateral entities become classes with typed attributes, the many-to-many becomes an association class, and multiplicities match the relationship cardinalities
Related knobits
Builds on
- K-906 Explains UML class diagrams as a data modelling scheme
- K-903 Explains data modelling notations
- K-1807 Names the notation of a UML class diagram
All 59 knobits of Derive the logical data model.
Source: DAMA-DMBOK2 chapter 5, element(s) DM-11: a performance at CDMP Practitioner level