Triple
T38477607
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Avowed |
E915587
|
entity |
| Predicate | trailerType |
P191430
|
FINISHED |
| Object | cinematic reveal trailer |
—
|
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: cinematic reveal trailer | Statement: [Avowed, trailerType, cinematic reveal trailer]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: trailerType Context triple: [Avowed, trailerType, cinematic reveal trailer]
-
A.
trailerField
Indicates that one entity is a field or attribute specifically associated with a trailer (such as a trailer record, object, or data structure).
-
B.
hasTrailerCar
Indicates that one vehicle is connected to and pulling another vehicle configured as a trailer car.
-
C.
carriageType
Indicates the specific kind or category of carriage associated with or used in relation to an entity.
-
D.
towingCapability
Indicates the maximum load or object weight that one entity is able to pull or tow.
-
E.
transmissionType
Indicates the method or medium through which something is transmitted or conveyed from one entity to another.
- 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_69f76e8ff5cc8190a88803369183845e |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69fcdf2394748190b35cead3e208447d |
completed | May 7, 2026, 6:51 p.m. |
| PD | Predicate disambiguation | batch_69fcdbe344ec8190a0471911952f4b82 |
completed | May 7, 2026, 6:37 p.m. |
| PDg | Predicate description generation | batch_69fcdf22ab8881908b257f16522920c5 |
completed | May 7, 2026, 6:51 p.m. |
Created at: May 3, 2026, 4:31 p.m.