Triple
T8910405
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sheppey Crossing |
E212166
|
entity |
| Predicate | numberOfInjuredIn2013Crash |
P63693
|
FINISHED |
| Object | over 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: over 60 | Statement: [Sheppey Crossing, numberOfInjuredIn2013Crash, over 60]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfInjuredIn2013Crash Context triple: [Sheppey Crossing, numberOfInjuredIn2013Crash, over 60]
-
A.
numberOfVictimsInjured
chosen
Indicates the count of victims who sustained injuries as a result of the event or incident.
-
B.
injuredIn
Indicates that an entity sustained an injury as a result of a specified event, situation, or action.
-
C.
involvedInAccident
Indicates that an entity participated in, was affected by, or was otherwise a party to a specific accident or collision event.
-
D.
hasInjuredPerson
Indicates that an entity has a person who has been harmed or injured associated with it.
-
E.
numberOfSeats2013
Indicates the quantity of seats associated with an entity specifically for the year 2013.
- 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_69ca839255248190b43984294abd92ae |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc65227d008190b13ba162d0b3c9d1 |
completed | April 1, 2026, 12:21 a.m. |
| PD | Predicate disambiguation | batch_69cc5ecf55248190a29f00fbf99f13c4 |
completed | March 31, 2026, 11:54 p.m. |
Created at: March 30, 2026, 6:55 p.m.