Triple
T33670290
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cole Trickle |
E862598
|
entity |
| Predicate | carManufacturerInFilm |
P66170
|
FINISHED |
| Object | Chevrolet Lumina |
—
|
NE NERFINISHED |
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: Chevrolet Lumina | Statement: [Cole Trickle, carManufacturerInFilm, Chevrolet Lumina]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: carManufacturerInFilm Context triple: [Cole Trickle, carManufacturerInFilm, Chevrolet Lumina]
-
A.
carManufacturer
chosen
Indicates that one entity is the company that produces or manufactures the car represented by the other entity.
-
B.
vehicleDrivenInFiction
Indicates that a vehicle is depicted as being driven or operated within a fictional work or narrative.
-
C.
hasFictionalVehicle
Indicates that one entity possesses, controls, or is associated with a vehicle that exists only in a fictional or imaginary context.
-
D.
carModel
Indicates the specific model designation of a car within a particular make or brand.
-
E.
vehicleBrandAssociated
Indicates that there is an association or linkage between a vehicle and a particular brand.
- 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_69f34984c4008190bb82f33a7819da64 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69f6fa3b3c7c8190abbb9926e0a106fa |
completed | May 3, 2026, 7:33 a.m. |
| PD | Predicate disambiguation | batch_69f6f96dd4c8819093d6a7bd046a9ad5 |
completed | May 3, 2026, 7:29 a.m. |
Created at: May 1, 2026, 1:42 a.m.