Triple
T3832455
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Motor Trend |
E91043
|
entity |
| Predicate | CarOfTheYearIs |
P52256
|
FINISHED |
| Object | annual award for new cars |
—
|
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: annual award for new cars | Statement: [Motor Trend, CarOfTheYearIs, annual award for new cars]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: CarOfTheYearIs Context triple: [Motor Trend, CarOfTheYearIs, annual award for new cars]
-
A.
carModel
Indicates the specific model designation of a car within a particular make or brand.
-
B.
notableCar
Indicates that the subject is a car recognized for its significance, prominence, or special interest (e.g., historically, culturally, or technically).
-
C.
isOneOfBestSellingCars
Indicates that the car is among the top-selling cars within a specified market or time period.
-
D.
demonstrationYear
Indicates the year in which a demonstration, display, or public showing of something took place.
-
E.
raceYear
Indicates the specific calendar year in which a particular race event takes place.
- 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_69aed960b538819096561c8ed448dec9 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aeeb8787bc8190819a7af975b609df |
completed | March 9, 2026, 3:47 p.m. |
| PD | Predicate disambiguation | batch_69aee74c2e04819094b94b3c0bac1806 |
completed | March 9, 2026, 3:29 p.m. |
| PDg | Predicate description generation | batch_69aeeb828fb08190901d51edbe8bd304 |
completed | March 9, 2026, 3:47 p.m. |
Created at: March 9, 2026, 3:17 p.m.