Triple
T5741821
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Runway magazine |
E126631
|
entity |
| Predicate | visualStyleInFilm |
P41012
|
FINISHED |
| Object | glamorous office interiors |
—
|
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: glamorous office interiors | Statement: [Runway magazine, visualStyleInFilm, glamorous office interiors]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: visualStyleInFilm Context triple: [Runway magazine, visualStyleInFilm, glamorous office interiors]
-
A.
hasFilmStyle
chosen
Indicates that a film exhibits or is characterized by a particular cinematic style or aesthetic approach.
-
B.
cinematicContext
Indicates the relationship in which something is situated within, shaped by, or relevant to the circumstances, style, or conventions of cinema or film.
-
C.
filmSetting
Indicates the place, time, or environment in which the events of a film are set or take place.
-
D.
visualEffect
Indicates that one entity produces, modifies, or is associated with a particular visual effect on another entity or within a scene.
-
E.
filmingTechnique
Indicates the specific method or style used to capture visual content during the filming process.
- 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_69c0083179548190b384b0bf3c08ca4d |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c02b52663c8190ab44258468d4296d |
completed | March 22, 2026, 5:48 p.m. |
| PD | Predicate disambiguation | batch_69c021ca61688190875bd6107161c284 |
completed | March 22, 2026, 5:07 p.m. |
Created at: March 22, 2026, 3:48 p.m.