Triple
T30160480
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sheppey Crossing pile-up |
E766649
|
entity |
| Predicate | numberOfVehiclesInvolved |
P22510
|
FINISHED |
| Object | more than 100 |
—
|
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: more than 100 | Statement: [Sheppey Crossing pile-up, numberOfVehiclesInvolved, more than 100]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfVehiclesInvolved Context triple: [Sheppey Crossing pile-up, numberOfVehiclesInvolved, more than 100]
-
A.
numberOfVehicles
chosen
Indicates the total count of vehicles associated with a given entity or context.
-
B.
numberOfTrainsInvolved
Indicates the count of trains that are involved in a particular event, situation, or incident.
-
C.
utilityInvolved
Indicates that a utility service or provider is involved in, associated with, or plays a role in the referenced situation or relationship.
-
D.
numberOfOfficersInvolved
Indicates the total count of officers who participated in or were involved in a particular event or action.
-
E.
numberOfPassengerCars
Indicates the total count of passenger cars associated with or contained in a given 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_69f2247a968881909d79c18f2bfcb275 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_6a0061454944819088a0babfe60f69fc |
completed | May 10, 2026, 10:43 a.m. |
| PD | Predicate disambiguation | batch_6a0060b9ee108190b91e8d99a16f2b30 |
completed | May 10, 2026, 10:40 a.m. |
Created at: April 29, 2026, 7:21 p.m.