Triple
T20746558
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ed Crane |
E510598
|
entity |
| Predicate | filmStyleContext |
P61780
|
FINISHED |
| Object | black-and-white cinematography |
—
|
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: black-and-white cinematography | Statement: [Ed Crane, filmStyleContext, black-and-white cinematography]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: filmStyleContext Context triple: [Ed Crane, filmStyleContext, black-and-white cinematography]
-
A.
filmTypeContext
Indicates the contextual relationship between a film and its type or category within a specific classification or usage setting.
-
B.
cinematicContext
chosen
Indicates the relationship in which something is situated within, shaped by, or relevant to the circumstances, style, or conventions of cinema or film.
-
C.
hasFilmStyle
Indicates that a film exhibits or is characterized by a particular cinematic style or aesthetic approach.
-
D.
filmSetting
Indicates the place, time, or environment in which the events of a film are set or take place.
-
E.
directorialStyle
Indicates the characteristic manner or approach a director consistently uses in creating and shaping their works.
- 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_69e0b4c845e88190b4c5f3ae79291182 |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c225c564819088f2461467698095 |
completed | April 21, 2026, 12:17 a.m. |
| PD | Predicate disambiguation | batch_69e5c0509608819080cdbf47fcddfe36 |
completed | April 20, 2026, 5:57 a.m. |
Created at: April 16, 2026, 12:33 p.m.