Triple
T25511517
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nicole Bonnet |
E639392
|
entity |
| Predicate | screenGenreArchetype |
P80218
|
FINISHED |
| Object | Audrey Hepburn romantic-comedy heroine |
—
|
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: Audrey Hepburn romantic-comedy heroine | Statement: [Nicole Bonnet, screenGenreArchetype, Audrey Hepburn romantic-comedy heroine]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: screenGenreArchetype Context triple: [Nicole Bonnet, screenGenreArchetype, Audrey Hepburn romantic-comedy heroine]
-
A.
visualGenre
Indicates the visual or stylistic category to which something belongs, such as its artistic or cinematic genre.
-
B.
targetGenre
Indicates the genre that something is specifically aimed at, categorized under, or intended to belong to.
-
C.
portraysCharacterInGenre
chosen
Indicates that an entity depicts or plays a character within works belonging to a specified genre.
-
D.
tvGenre
Indicates the genre or category to which a television show or program belongs.
-
E.
screenDebutGenreForActor
Indicates the genre in which an actor made their first on-screen appearance.
- 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.