Triple
T20030302
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shipton-on-Cherwell train crash |
E495103
|
entity |
| Predicate | numberOfInjuries |
P25887
|
FINISHED |
| Object | 60+ |
—
|
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: 60+ | Statement: [Shipton-on-Cherwell train crash, numberOfInjuries, 60+]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfInjuries Context triple: [Shipton-on-Cherwell train crash, numberOfInjuries, 60+]
-
A.
hasInjuries
Indicates that an entity has sustained one or more physical or bodily injuries.
-
B.
injuriesApprox
chosen
Indicates an approximate or estimated number or extent of injuries associated with an event or entity.
-
C.
injuredIn
Indicates that an entity sustained an injury as a result of a specified event, situation, or action.
-
D.
numberOfPeopleLaterDyingOfInjuriesConsidered
Indicates the number of people who subsequently died from injuries that were previously evaluated or taken into account.
-
E.
injuryStatus
Indicates the condition or state of harm, damage, or physical injury affecting an entity.
- 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_69da626bfd288190aa5d65098b6433ae |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e66291a00c8190b0b895909f32d623 |
completed | April 20, 2026, 5:29 p.m. |
| PD | Predicate disambiguation | batch_69e54ce752748190a0a1ffddd0372271 |
completed | April 19, 2026, 9:45 p.m. |
Created at: April 11, 2026, 3:36 p.m.