Triple
T36109177
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2019 World Car of the Year |
E1044442
|
entity |
| Predicate | manufacturerOfWinner |
P146459
|
FINISHED |
| Object | Jaguar Land Rover |
—
|
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: Jaguar Land Rover | Statement: [2019 World Car of the Year, manufacturerOfWinner, Jaguar Land Rover]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: manufacturerOfWinner Context triple: [2019 World Car of the Year, manufacturerOfWinner, Jaguar Land Rover]
-
A.
winningManufacturer
chosen
Indicates that a manufacturer is the one that won a particular competition, contract, award, or selection process.
-
B.
manufacturerOfWinningCar
Indicates that an entity is the manufacturer responsible for producing the car that won a particular race or competition.
-
C.
engineManufacturerOfWinningCar
Indicates that an entity is the manufacturer of the engine used in the car that won a particular race or competition.
-
D.
chassisManufacturerOfWinningCar
Indicates that a manufacturer built the chassis of the car that won a particular race or competition.
-
E.
runnerUpManufacturer
Indicates that a manufacturer finished in second place relative to others in a specified competition, ranking, or evaluation.
- 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_69f76e344a4c8190af3858c6d78ba88f |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69f7cec454a88190a9f3bbee2b856636 |
completed | May 3, 2026, 10:40 p.m. |
| PD | Predicate disambiguation | batch_69f7c8977c288190997a892ec5f756ed |
completed | May 3, 2026, 10:13 p.m. |
Created at: May 3, 2026, 4:08 p.m.