Triple
T36524389
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vortex |
E900259
|
entity |
| Predicate | usesCinematicDevice |
P145815
|
FINISHED |
| Object | simultaneous dual perspectives |
—
|
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: simultaneous dual perspectives | Statement: [Vortex, usesCinematicDevice, simultaneous dual perspectives]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesCinematicDevice Context triple: [Vortex, usesCinematicDevice, simultaneous dual perspectives]
-
A.
hasCinematicFeature
chosen
Indicates that something possesses a specific cinematic characteristic, quality, or element related to film or visual storytelling.
-
B.
supportsCinematicMode
Indicates that one entity provides or enables a cinematic mode feature for another entity.
-
C.
hasCinematicMovement
Indicates that an entity exhibits or involves a style of movement characteristic of cinematic techniques, such as dynamic camera-like motion or film-inspired transitions.
-
D.
usesSingleCamera
Indicates that an entity performs an action or operates in a context using only one camera rather than multiple cameras.
-
E.
usesCameraType
Indicates that one entity employs or operates a specific type or category of camera.
- 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_69f76e5eedb88190a393b8c623f71dd7 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69fe1fd637c08190aa95cd2478c278cb |
completed | May 8, 2026, 5:39 p.m. |
| PD | Predicate disambiguation | batch_69fe19344bb481909b5e2144155e4add |
completed | May 8, 2026, 5:11 p.m. |
Created at: May 3, 2026, 4:11 p.m.