Triple
T700427
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | French |
E13984
|
entity |
| Predicate | hasT-VDistinction |
P13255
|
FINISHED |
| Object | tu–vous distinction |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: tu–vous distinction | Statement: [French, hasT-VDistinction, tu–vous distinction]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasT-VDistinction Context triple: [French, hasT-VDistinction, tu–vous distinction]
-
A.
hasRegisterDistinction
chosen
Indicates that there is a meaningful difference in language register (e.g., formality or style) between the related linguistic forms or usages.
-
B.
hasDistinction
Indicates that one entity possesses, is awarded, or is recognized with a special honor, title, or mark of excellence in relation to another entity or context.
-
C.
uniformDistinction
Indicates that a clear and consistent difference is maintained between two or more entities within a given context.
-
D.
hasDefinitenessDistinction
Indicates that a language or system grammatically distinguishes between definite and indefinite (or otherwise specified) reference in its expressions.
-
E.
hasGenderDistinction
Indicates that a relationship, classification, or linguistic form differentiates entities based on gender categories.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69a493406c408190957eeec9048a8fb6 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a544e3608190ac315c7aa9f88e7e |
completed | March 1, 2026, 8:44 p.m. |
| PD | Predicate disambiguation | batch_69a4a4ec8c748190b198492a0eea4445 |
completed | March 1, 2026, 8:43 p.m. |
Created at: March 1, 2026, 7:36 p.m.