Triple
T17036074
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Telling Lies |
E413322
|
entity |
| Predicate | hasCameraWork |
P125595
|
FINISHED |
| Object | live-action 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: live-action cinematography | Statement: [Telling Lies, hasCameraWork, live-action cinematography]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCameraWork Context triple: [Telling Lies, hasCameraWork, live-action cinematography]
-
A.
hasCamera
Indicates that an entity is equipped with or possesses a camera.
-
B.
hasPhotographicActivity
Indicates that one entity engages in or is involved with photographic activity in relation to another entity or context.
-
C.
hasPhotographicProcess
Indicates that something is associated with, created by, or characterized through a specific photographic process or technique.
-
D.
hasPhotogenicFeature
Indicates that an entity possesses a visual characteristic or attribute that is especially attractive or appealing when photographed.
-
E.
usesCameraType
Indicates that one entity employs or operates a specific type or category of camera.
- 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_69d886cd18288190b006abab23f811b7 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3d8f26f50819085dfd0fbecd6394d |
completed | April 18, 2026, 7:18 p.m. |
| PD | Predicate disambiguation | batch_69e35d5be7f48190af9db67a1e23850f |
completed | April 18, 2026, 10:30 a.m. |
| PDg | Predicate description generation | batch_69e3753f93c88190808fec5692f66699 |
completed | April 18, 2026, 12:12 p.m. |
Created at: April 10, 2026, 5:33 a.m.