Triple
T4214279
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2016 Motor Trend Car of the Year |
E93975
|
entity |
| Predicate | winnerModelYear |
P54787
|
FINISHED |
| Object | 2016 |
—
|
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: 2016 | Statement: [2016 Motor Trend Car of the Year, winnerModelYear, 2016]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: winnerModelYear Context triple: [2016 Motor Trend Car of the Year, winnerModelYear, 2016]
-
A.
modelYears
Indicates the association between a product (often a vehicle or device) and the specific calendar years in which that model version was produced or marketed.
-
B.
raceYear
Indicates the specific calendar year in which a particular race event takes place.
-
C.
lastAppearanceYear
Indicates the calendar year in which an entity made its most recent appearance.
-
D.
demonstrationYear
Indicates the year in which a demonstration, display, or public showing of something took place.
-
E.
yearFirstDelivered
Indicates the calendar year in which something (such as a product, service, or item) was first delivered or made available.
- 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_69b3451743608190808f41d17ccf2650 |
completed | March 12, 2026, 10:58 p.m. |
| NER | Named-entity recognition | batch_69b34e098da881909a0cc339cc186627 |
completed | March 12, 2026, 11:36 p.m. |
| PD | Predicate disambiguation | batch_69b347efd9b08190bb50f82e4e7fe06d |
completed | March 12, 2026, 11:10 p.m. |
| PDg | Predicate description generation | batch_69b34e04ef1c81908bb34ae1cbfab1e6 |
completed | March 12, 2026, 11:36 p.m. |
Created at: March 12, 2026, 11:04 p.m.