Recognises an ambiguous pronoun in a rule statement
A pronoun with more than one possible referent. Reads fine, cannot be implemented.
Can AI do this?
AI does this well Most of the tested AI models master this. 9 of 10 models master it.
| Model | Tier | Probability of mastery | Status |
|---|---|---|---|
| gemini-4-argon | Flagship | 100% | Mastered |
| gemini-3.1-pro-preview | Flagship | 99% | Mastered |
| gpt-6-astra | Flagship | 100% | Mastered |
| claude-fable-5-1 | Flagship | 99% | Mastered |
| gemini-3.8-flash | Mid tier | 99% | Mastered |
| gpt-6.1-sol | Mid tier | 100% | Mastered |
| claude-sonnet-5-5 | Mid tier | 99% | Mastered |
| gemini-3.5-flash-lite | Low cost | 42% | Not shown |
| gpt-6-luna | Low cost | 100% | Mastered |
| claude-haiku-5-5 | Low cost | 99% | 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 pronoun and its candidate referents
How it is assessed: Flags the deficient case and passes the acceptable one. Deficient: "The settled amount must not exceed the instructed amount, and it must be positive" Acceptable: "The settled amount must not exceed the instructed amount. The settled amount must be greater than zero." (RUL-020)
Also known as: Ambiguous pronoun (data rule)
Related knobits
Builds on
All 28 knobits of Document data rules as SBVR fact types and rule statements.
Source: Review checklist 03-data-rules, row 16