Triple
T31294996
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Beaux |
E798046
|
entity |
| Predicate | grammaticalNumberInFrench |
P11612
|
FINISHED |
| Object | plural |
—
|
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: plural | Statement: [Beaux, grammaticalNumberInFrench, plural]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: grammaticalNumberInFrench Context triple: [Beaux, grammaticalNumberInFrench, plural]
-
A.
hasGrammaticalNumber
chosen
Indicates that an expression is associated with a specific grammatical number category (such as singular, plural, or dual) in a language.
-
B.
FrenchForm
Indicates that one entity is a form, version, or expression of another specifically in the French language.
-
C.
numberOfGrammaticalCases
Indicates the relationship that specifies how many distinct grammatical cases a language or linguistic system possesses.
-
D.
grammaticalCaseOfFrancorum
Indicates that something is in the grammatical case associated with the Latin genitive plural form "Francorum" (i.e., expressing "of the Franks").
-
E.
fractionalUnitNameInFrench
Indicates the French-language name used for a fractional unit associated with another quantity or measure.
- 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_69f224dfde288190af313f3c221c857e |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f69edbb7648190bd89c57e0932eac1 |
completed | May 3, 2026, 1:03 a.m. |
| PD | Predicate disambiguation | batch_69f69d1bf8cc8190a78dfa5ab00daf3a |
completed | May 3, 2026, 12:55 a.m. |
Created at: April 29, 2026, 9:14 p.m.