Triple
T32509930
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Faye Dunaway as Wilhelmina Cooper |
E830902
|
entity |
| Predicate | portrayalInGenre |
P80218
|
FINISHED |
| Object | fashion industry film |
—
|
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: fashion industry film | Statement: [Faye Dunaway as Wilhelmina Cooper, portrayalInGenre, fashion industry film]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: portrayalInGenre Context triple: [Faye Dunaway as Wilhelmina Cooper, portrayalInGenre, fashion industry film]
-
A.
portraysCharacterInGenre
chosen
Indicates that an entity depicts or plays a character within works belonging to a specified genre.
-
B.
portrayalFeature
Indicates that one entity serves as a characteristic, aspect, or attribute highlighted in the depiction or representation of another entity.
-
C.
portraysGenreConvention
Indicates that an entity depicts or exemplifies a characteristic convention, trope, or stylistic feature associated with a particular genre.
-
D.
portrayalFormat
Indicates the medium or format in which something is portrayed or represented (e.g., painting, sculpture, film, digital).
-
E.
depictsGenre
Indicates that one entity visually represents or portrays the genre category associated with another entity.
- 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_69f3492318348190ba37fb6b5f1d67f4 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69fe5d58c3e48190910aa3c23485e2c4 |
completed | May 8, 2026, 10:02 p.m. |
| PD | Predicate disambiguation | batch_69fe5c92090c8190bcfa412c0a3619df |
completed | May 8, 2026, 9:58 p.m. |
Created at: May 1, 2026, 1 a.m.