Recognises a stored derivable attribute
An attribute whose value can be computed from other attributes, stored and writable alongside them, so the stored and computed values can disagree.
Can AI do this?
AI does this well Most of the tested AI models master this. 10 of 10 models 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 | 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: The attribute, the attributes it is computed from, and whether it is a snapshot instead
How it is assessed: Flags the deficient case and passes the acceptable one. Deficient: Balance with Ledger Balance Amount, Hold Amount and Available Balance Amount all captured; Repayment Schedule with Total Repayment Amount alongside its instalments Acceptable: Available balance derived, with derivation and the rule cited (available balance = ledger balance − holds); total repayment stored only as the amount contracted, with derivation saying it is not recomputed (see data rules row 6)
Also known as: Stored derivable attribute (logical model)
Related knobits
Builds on
Needed for
All 56 knobits of Derive the logical data model.
Source: Review checklist 05-logical, row 10; S&W 2.5.3