Triple
T863839
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dont Look Back |
E18655
|
entity |
| Predicate | cameraStyle |
P20442
|
FINISHED |
| Object | handheld 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: handheld cinematography | Statement: [Dont Look Back, cameraStyle, handheld cinematography]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: cameraStyle Context triple: [Dont Look Back, cameraStyle, handheld cinematography]
-
A.
hasCamera
Indicates that an entity is equipped with or possesses a camera.
-
B.
meetsInCamera
Indicates that two or more entities are physically present together in the same camera frame or shot at the same time.
-
C.
hasOpticalImageStabilization
Indicates that a device or component includes a feature that reduces image blur caused by camera movement during capture.
-
D.
givesCanon
Indicates that one entity provides or establishes an official or authoritative version (canon) of something for another entity or context.
-
E.
capturedEquipment
Indicates that one party has taken possession of another party’s equipment, typically as a result of conflict, competition, or enforcement.
- F. None of above. chosen
Provenance (4 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_69a4938ce8688190a24bdfef82ba7d21 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4ac6956488190a5644cdd5b55684f |
completed | March 1, 2026, 9:15 p.m. |
| PD | Predicate disambiguation | batch_69a4aa86065881909d477e26fdd84d45 |
completed | March 1, 2026, 9:07 p.m. |
| PDg | Predicate description generation | batch_69a4ab797b1081908f034d216649bb83 |
completed | March 1, 2026, 9:11 p.m. |
Created at: March 1, 2026, 7:39 p.m.