Triple
T20276169
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mary in The Passion of the Christ |
E503021
|
entity |
| Predicate | cameraTreatment |
P20442
|
FINISHED |
| Object | frequent close-ups emphasizing emotional reaction |
—
|
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: frequent close-ups emphasizing emotional reaction | Statement: [Mary in The Passion of the Christ, cameraTreatment, frequent close-ups emphasizing emotional reaction]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: cameraTreatment Context triple: [Mary in The Passion of the Christ, cameraTreatment, frequent close-ups emphasizing emotional reaction]
-
A.
cameraSystem
Indicates a relationship where an entity functions as or is part of a camera-based monitoring or imaging system.
-
B.
cameraConfiguration
Indicates the specific setup or arrangement of a camera’s parameters or components in a given context.
-
C.
cameraStyle
chosen
Indicates the characteristic visual approach or technique used by a camera in capturing or presenting imagery.
-
D.
supportsCameraControl
Indicates that one entity provides functionality for another entity to remotely manage or adjust camera settings or operations.
-
E.
cameraTechnology
Indicates the type or characteristics of camera-related technology associated with an 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_69e0b4b0e79c8190bd61f22ef1329fa8 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e675e3df68819096fb859bc92a0da1 |
completed | April 20, 2026, 6:52 p.m. |
| PD | Predicate disambiguation | batch_69e55b1e5e1c8190ba8a5544b1db9e1d |
completed | April 19, 2026, 10:45 p.m. |
Created at: April 16, 2026, 10:32 a.m.