Triple
T8910406
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sheppey Crossing |
E212166
|
entity |
| Predicate | fatalitiesIn2013Crash |
P60545
|
FINISHED |
| Object | 0 |
—
|
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: 0 | Statement: [Sheppey Crossing, fatalitiesIn2013Crash, 0]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fatalitiesIn2013Crash Context triple: [Sheppey Crossing, fatalitiesIn2013Crash, 0]
-
A.
numberOfFatalAccidents
Indicates the total count of accidents within a given context that resulted in at least one fatality.
-
B.
fatalitiesOnboard
Indicates that the relationship specifies the number of people who died among those present on a particular vehicle or craft.
-
C.
fatalitiesCategory
Indicates the classification of deaths associated with an event, incident, or condition into a specific category or severity level.
-
D.
hadNoFatalities
chosen
Indicates that the referenced event, incident, or situation resulted in zero deaths.
-
E.
numberOfVictimsInjured
Indicates the count of victims who sustained injuries as a result of the event or incident.
- 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.