Triple
T6043073
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Brandon Lee |
E134594
|
entity |
| Predicate | causeOfOnSetAccident |
P14656
|
FINISHED |
| Object | defective blank cartridge and projectile in prop gun |
—
|
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: defective blank cartridge and projectile in prop gun | Statement: [Brandon Lee, causeOfOnSetAccident, defective blank cartridge and projectile in prop gun]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: causeOfOnSetAccident Context triple: [Brandon Lee, causeOfOnSetAccident, defective blank cartridge and projectile in prop gun]
-
A.
causeOfInjury
chosen
Indicates that one entity is the source or reason that another entity sustained an injury.
-
B.
resultOfAccident
Indicates that something exists or occurs as a consequence or outcome of an accident.
-
C.
accidentType
Indicates the specific category or kind of accident associated with an event or incident.
-
D.
accident
Indicates an unintended, unforeseen event or mishap occurring, often resulting in damage, injury, or disruption.
-
E.
siteOfAccident
Indicates the location where an accident occurred.
- 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_69c00876a69881908088a2626d3b2666 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c056e108fc81908775d176ff960fad |
completed | March 22, 2026, 8:53 p.m. |
| PD | Predicate disambiguation | batch_69c049eb52a08190ac10fd703735f5aa |
completed | March 22, 2026, 7:58 p.m. |
Created at: March 22, 2026, 4:08 p.m.