Triple
T19425599
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Puerto Vallarta–San Francisco–Seattle |
E485971
|
entity |
| Predicate | associatedWithAccident |
P32122
|
FINISHED |
| Object | Alaska Airlines Flight 261 crash |
—
|
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: Alaska Airlines Flight 261 crash | Statement: [Puerto Vallarta–San Francisco–Seattle, associatedWithAccident, Alaska Airlines Flight 261 crash]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: associatedWithAccident Context triple: [Puerto Vallarta–San Francisco–Seattle, associatedWithAccident, Alaska Airlines Flight 261 crash]
-
A.
involvedInAccident
chosen
Indicates that an entity participated in, was affected by, or was otherwise a party to a specific accident or collision event.
-
B.
hasAccidentAt
Indicates that an accident involving a subject occurs at a specific location or time.
-
C.
causedAccident
Indicates that one entity is responsible for bringing about or initiating an accident involving another entity or situation.
-
D.
resultOfAccident
Indicates that something exists or occurs as a consequence or outcome of an accident.
-
E.
hasModeOfTransportInAccident
Indicates that a specific mode of transport was involved in an accident associated with the given entity.
- 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_69d8e8d688f881909c85104a62e09d8a |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e63218772c8190a48b6cb01bd12b73 |
completed | April 20, 2026, 2:03 p.m. |
| PD | Predicate disambiguation | batch_69e4fd68b1f881908d273de1fee81a75 |
completed | April 19, 2026, 4:06 p.m. |
Created at: April 10, 2026, 1:37 p.m.