Triple
T30160484
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sheppey Crossing pile-up |
E766649
|
entity |
| Predicate | numberOfSeriousInjuries |
P63693
|
FINISHED |
| Object | 8 |
—
|
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: 8 | Statement: [Sheppey Crossing pile-up, numberOfSeriousInjuries, 8]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfSeriousInjuries Context triple: [Sheppey Crossing pile-up, numberOfSeriousInjuries, 8]
-
A.
numberOfVictimsInjured
chosen
Indicates the count of victims who sustained injuries as a result of the event or incident.
-
B.
numberOfFatalAccidents
Indicates the total count of accidents within a given context that resulted in at least one fatality.
-
C.
numberOfPeopleLaterDyingOfInjuriesConsidered
Indicates the number of people who subsequently died from injuries that were previously evaluated or taken into account.
-
D.
accidentSeverity
Indicates the level or degree of seriousness associated with an accident.
-
E.
injuredIn
Indicates that an entity sustained an injury as a result of a specified event, situation, or action.
- 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_6a0062e6bd788190a7b4f3e5befb5cbb |
completed | May 10, 2026, 10:50 a.m. |
| PD | Predicate disambiguation | batch_6a0061989d188190b4815b2de3e8676f |
completed | May 10, 2026, 10:44 a.m. |
Created at: April 29, 2026, 7:21 p.m.