Triple
T25511506
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nicole Bonnet |
E639392
|
entity |
| Predicate | fashionDesignerOfCostumes |
P36430
|
FINISHED |
| Object | Givenchy |
—
|
NE NERFINISHED |
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: Givenchy | Statement: [Nicole Bonnet, fashionDesignerOfCostumes, Givenchy]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fashionDesignerOfCostumes Context triple: [Nicole Bonnet, fashionDesignerOfCostumes, Givenchy]
-
A.
costumeDesignerOfWork
Indicates that an entity serves as the costume designer responsible for the costumes in a particular creative work.
-
B.
designedCostumesFor
chosen
Indicates that one entity created or planned the costumes used by another entity, typically for a performance, production, or event.
-
C.
costumeDesignEmphasisOn
Indicates that a costume design places particular focus or priority on a specified element, style, feature, or thematic aspect.
-
D.
knownForCostumes
Indicates that an entity is recognized or notable specifically for its costumes, such as their design, creation, or distinctive use.
-
E.
costumeDesignStyle
Indicates the stylistic approach or aesthetic characteristics used in designing a costume for a character or production.
- 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_69e75dbd09308190b6b5f0afdc12ec6d |
completed | April 21, 2026, 11:21 a.m. |
| NER | Named-entity recognition | batch_69f5f80b05ac8190a4a0cd75e8717917 |
completed | May 2, 2026, 1:11 p.m. |
| PD | Predicate disambiguation | batch_69f468421ba08190880eac99135e5970 |
completed | May 1, 2026, 8:45 a.m. |
Created at: April 21, 2026, 2:49 p.m.