Triple
T12120157
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Amagasaki derailment |
E288671
|
entity |
| Predicate | affectedCars |
P103391
|
FINISHED |
| Object | first car |
—
|
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: first car | Statement: [Amagasaki derailment, affectedCars, first car]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: affectedCars Context triple: [Amagasaki derailment, affectedCars, first car]
-
A.
relatedVehicle
Indicates that there exists an associated or connected vehicle that has a relevant relationship to the primary entity.
-
B.
notableCar
Indicates that the subject is a car recognized for its significance, prominence, or special interest (e.g., historically, culturally, or technically).
-
C.
producedVehicle
Indicates that one entity manufactured or created a particular vehicle.
-
D.
numberOfPassengerCars
Indicates the total count of passenger cars associated with or contained in a given entity or context.
-
E.
alsoCarries
Indicates that an entity, in addition to other items or responsibilities it has, carries another specified item or load as well.
- 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_69d6ab4b5e4c81909950b17151eb0951 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d9164ada5081908676bd9e5947268a |
completed | April 10, 2026, 3:24 p.m. |
| PD | Predicate disambiguation | batch_69d9150497408190921334d21503375a |
completed | April 10, 2026, 3:19 p.m. |
| PDg | Predicate description generation | batch_69d916481a008190ae66677b9e6dd961 |
completed | April 10, 2026, 3:24 p.m. |
Created at: April 8, 2026, 9:49 p.m.