Triple
T12120150
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Amagasaki derailment |
E288671
|
entity |
| Predicate | estimatedTrainSpeedAtDerailment |
P87957
|
FINISHED |
| Object | approximately 116 km/h |
—
|
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: approximately 116 km/h | Statement: [Amagasaki derailment, estimatedTrainSpeedAtDerailment, approximately 116 km/h]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: estimatedTrainSpeedAtDerailment Context triple: [Amagasaki derailment, estimatedTrainSpeedAtDerailment, approximately 116 km/h]
-
A.
speedAtDerailmentApprox
chosen
Indicates the approximate speed an entity was traveling at the moment it derailed.
-
B.
numberOfTrainsInvolved
Indicates the count of trains that are involved in a particular event, situation, or incident.
-
C.
speedClassOfTrains
Indicates the classification of trains based on their operating speed or speed category.
-
D.
relativeSpeedComparedToConventionalTrains
Indicates how the speed of something compares to that of conventional trains, typically expressing whether it is faster, slower, or similar.
-
E.
siteOfAccident
Indicates the location where an accident occurred.
- 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_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. |
Created at: April 8, 2026, 9:49 p.m.