Triple
T38117780
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Oldsmobile Toronado |
E951837
|
entity |
| Predicate | firstModelYearEngineDisplacement |
P7998
|
FINISHED |
| Object | 425 cu in V8 |
—
|
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: 425 cu in V8 | Statement: [Oldsmobile Toronado, firstModelYearEngineDisplacement, 425 cu in V8]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: firstModelYearEngineDisplacement Context triple: [Oldsmobile Toronado, firstModelYearEngineDisplacement, 425 cu in V8]
-
A.
availableEngineDisplacement
Indicates the range or specific values of engine displacement that are offered or applicable for a given entity.
-
B.
engineDisplacement
chosen
Indicates the total volume swept by all the pistons inside an engine’s cylinders during one complete cycle.
-
C.
hasEngineDisplacementClass
Indicates a relationship where a vehicle or engine is assigned to a category based on the size or volume of its engine displacement.
-
D.
originalEngineModel
Indicates that one engine is the original or initial model from which another engine or engine variant is derived.
-
E.
firstModelIntroduced
Indicates that one entity is the earliest or original model introduced in relation to another entity or context.
- 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_69f76f07734c8190814e937e12257a78 |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_6a037df1223c8190a5d61e4f8e6fd613 |
completed | May 12, 2026, 7:22 p.m. |
| PD | Predicate disambiguation | batch_6a037a1ad6c48190bfe35d350c1b4751 |
completed | May 12, 2026, 7:06 p.m. |
Created at: May 3, 2026, 4:21 p.m.