Triple
T18780670
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | AMC Matador car-plane |
E459245
|
entity |
| Predicate | vehicleTypeInFilm |
P1776
|
FINISHED |
| Object | two-door coupe |
—
|
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: two-door coupe | Statement: [AMC Matador car-plane, vehicleTypeInFilm, two-door coupe]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: vehicleTypeInFilm Context triple: [AMC Matador car-plane, vehicleTypeInFilm, two-door coupe]
-
A.
hasFictionalVehicle
Indicates that one entity possesses, controls, or is associated with a vehicle that exists only in a fictional or imaginary context.
-
B.
starVehicleFor
Indicates that one entity serves as the primary or featured vehicle associated with another entity, such as a person, production, or event.
-
C.
vehicleType
chosen
Indicates the specific kind or category of vehicle associated with an entity (e.g., car, bus, bicycle).
-
D.
bodyTypeDepicted
Indicates that one entity visually represents or portrays the physical body type of another entity.
-
E.
engineUsedIn
Indicates that a particular engine is utilized as a component or power source within a specified system, device, or application.
- 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_69d8d396f54c8190ba49db31e8743842 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e5933ee89481908b647802961e4519 |
completed | April 20, 2026, 2:45 a.m. |
| PD | Predicate disambiguation | batch_69e48d1126e4819099607837ed5aadca |
completed | April 19, 2026, 8:06 a.m. |
Created at: April 10, 2026, 11:52 a.m.