Triple
T22668183
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chrysler P-body |
E559847
|
entity |
| Predicate | modelYearsCovered |
P4161
|
FINISHED |
| Object | 1987 |
—
|
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: 1987 | Statement: [Chrysler P-body, modelYearsCovered, 1987]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: modelYearsCovered Context triple: [Chrysler P-body, modelYearsCovered, 1987]
-
A.
modelYears
chosen
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.
conceptCarYear
Indicates the year in which a concept car was first introduced, presented, or designated.
-
C.
productionYears
Indicates the span of calendar years during which something was produced or manufactured.
-
D.
winnerModelYear
Indicates that a model was the winning model for a particular year.
-
E.
coversYearsTo
Indicates a temporal relationship where one entity spans, includes, or extends up to a specified year or range of years represented by the other entity.
- 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_69e2454a158c819093b8e35f5045efb6 |
completed | April 17, 2026, 2:35 p.m. |
| NER | Named-entity recognition | batch_69f1781de1d48190947cb1bb9d0890d9 |
completed | April 29, 2026, 3:16 a.m. |
| PD | Predicate disambiguation | batch_69ee62a6245881909506ff502da14137 |
completed | April 26, 2026, 7:08 p.m. |
Created at: April 17, 2026, 3:09 p.m.