Triple
T35151881
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | William Gary Busey |
E1015014
|
entity |
| Predicate | hasPartInjuryOrAccident |
P32122
|
FINISHED |
| Object | motorcycle accident in 1988 |
—
|
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: motorcycle accident in 1988 | Statement: [William Gary Busey, hasPartInjuryOrAccident, motorcycle accident in 1988]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPartInjuryOrAccident Context triple: [William Gary Busey, hasPartInjuryOrAccident, motorcycle accident in 1988]
-
A.
hasPlaceOfInjury
Indicates that an injury occurred at a specific place or location.
-
B.
hasInjuredPerson
Indicates that an entity has a person who has been harmed or injured associated with it.
-
C.
hasAccidentAt
Indicates that an accident involving a subject occurs at a specific location or time.
-
D.
hasInjuries
Indicates that an entity has sustained one or more physical or bodily injuries.
-
E.
involvedInAccident
chosen
Indicates that an entity participated in, was affected by, or was otherwise a party to a specific accident or collision event.
- 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_69f76ddb3a708190b521ba2970b17178 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69ffb69812808190a751853b30183e65 |
completed | May 9, 2026, 10:35 p.m. |
| PD | Predicate disambiguation | batch_69ffb63bdda88190a9dd8426dc0bad43 |
completed | May 9, 2026, 10:33 p.m. |
Created at: May 3, 2026, 4:02 p.m.