Triple
T10581254
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Berry Oakley died in a motorcycle accident in 1972 |
E249739
|
entity |
| Predicate | hasTypeOfAccident |
P1788
|
FINISHED |
| Object | traffic collision |
—
|
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: traffic collision | Statement: [Berry Oakley died in a motorcycle accident in 1972, hasTypeOfAccident, traffic collision]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTypeOfAccident Context triple: [Berry Oakley died in a motorcycle accident in 1972, hasTypeOfAccident, traffic collision]
-
A.
hasAccidentAt
Indicates that an accident involving a subject occurs at a specific location or time.
-
B.
accidentType
chosen
Indicates the specific category or kind of accident associated with an event or incident.
-
C.
causedAccident
Indicates that one entity is responsible for bringing about or initiating an accident involving another entity or situation.
-
D.
involvedInAccident
Indicates that an entity participated in, was affected by, or was otherwise a party to a specific accident or collision event.
-
E.
resultOfAccident
Indicates that something exists or occurs as a consequence or outcome of an accident.
- 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_69d381c9d3d48190a29ee491e1696a0e |
completed | April 6, 2026, 9:50 a.m. |
| NER | Named-entity recognition | batch_69d5275b2424819093331b3777f12beb |
completed | April 7, 2026, 3:48 p.m. |
| PD | Predicate disambiguation | batch_69d51907b2b881908ab9a8594688ee06 |
completed | April 7, 2026, 2:47 p.m. |
Created at: April 6, 2026, 12:39 p.m.