Triple
T32558432
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pursuit Special |
E832154
|
entity |
| Predicate | basedOnModelYear |
P105096
|
FINISHED |
| Object | 1973 |
—
|
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: 1973 | Statement: [Pursuit Special, basedOnModelYear, 1973]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: basedOnModelYear Context triple: [Pursuit Special, basedOnModelYear, 1973]
-
A.
basedOnYear
chosen
Indicates that something is determined, derived, or organized according to a specific year.
-
B.
basedOnCarGeneration
Indicates that one car is derived from, designed according to, or otherwise based on the generation or model series of another car.
-
C.
basedOnVehicle
Indicates that one entity is derived from, modeled after, or otherwise conceptually or functionally based on a particular vehicle.
-
D.
yearDeliveredApprox
Indicates that the year an item or entity was delivered is known only approximately, rather than as an exact calendar year.
-
E.
winnerModelYear
Indicates that a model was the winning model for a particular year.
- 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_69f34926b9848190ace47d2dd0a0de7c |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69fe7b1c506c8190869c1a22031e0571 |
completed | May 9, 2026, 12:09 a.m. |
| PD | Predicate disambiguation | batch_69fe796b2bdc8190a86980d44008f875 |
completed | May 9, 2026, 12:01 a.m. |
Created at: May 1, 2026, 1:03 a.m.