Explains naming conventions for data model objects
Names built from object, property and representation terms with class words and an agreed abbreviation list (ISO/IEC 11179).
Can AI do this?
People still lead Few or none of the tested AI models showed they master this. 0 of 10 models master it.
| Model | Tier | Probability of mastery | Status |
|---|---|---|---|
| gemini-4-argon | Flagship | 60% | Not shown |
| gemini-3.1-pro-preview | Flagship | 59% | Not shown |
| gpt-6-astra | Flagship | 59% | Not shown |
| claude-fable-5-1 | Flagship | 60% | Not shown |
| gemini-3.8-flash | Mid tier | 62% | Not shown |
| gpt-6.1-sol | Mid tier | 61% | Not shown |
| claude-sonnet-5-5 | Mid tier | 60% | Not shown |
| gemini-3.5-flash-lite | Low cost | 61% | Not shown |
| gpt-6-luna | Low cost | 59% | Not shown |
| claude-haiku-5-5 | Low cost | 61% | 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: Names built from object, property and representation terms with class words and an agreed abbreviation list (ISO/IEC 11179)
How it is assessed: Names the attributes of a sample entity by the convention and corrects non-conforming names
Related knobits
Needed for
- K-810 Recognises a truncated identifier taken as the real name
- K-811 Recognises a reserved word used as an identifier
- K-921 Explains data modelling and design standards
All knobits of Data modelling.
Source: DAMA-DMBOK2 chapter 5, section 4.1, 5.1.1