Triple
T6634636
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Woman (film) |
E150415
|
entity |
| Predicate | hasInterviewStyle |
P41012
|
FINISHED |
| Object | direct-to-camera close-up interviews |
—
|
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: direct-to-camera close-up interviews | Statement: [Woman (film), hasInterviewStyle, direct-to-camera close-up interviews]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasInterviewStyle Context triple: [Woman (film), hasInterviewStyle, direct-to-camera close-up interviews]
-
A.
hasAuthorInterviewRole
Indicates that an entity participates in an interview in the role of an author.
-
B.
interviewedBy
Indicates that an entity is the subject of an interview conducted by another entity.
-
C.
hasFilmStyle
chosen
Indicates that a film exhibits or is characterized by a particular cinematic style or aesthetic approach.
-
D.
hasGivenNumberOfInterviewsAbout
Indicates that an entity has conducted or participated in a specified number of interviews concerning another entity or topic.
-
E.
hasDramaticStyle
Indicates that an entity employs or is characterized by a theatrical, emotionally intense, or striking manner of expression or presentation.
- 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_69c687f0ceb08190bf40807bfc605fa5 |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6c308a08881908501c862b3029321 |
completed | March 27, 2026, 5:48 p.m. |
| PD | Predicate disambiguation | batch_69c6ad024860819084b9b535b136ede6 |
completed | March 27, 2026, 4:14 p.m. |
Created at: March 27, 2026, 1:59 p.m.