Triple
T27546743
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ara Zobayan |
E695382
|
entity |
| Predicate | flightOriginOnAccidentDay |
P87740
|
FINISHED |
| Object | John Wayne Airport, Orange County, California |
—
|
NE NERFINISHED |
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: John Wayne Airport, Orange County, California | Statement: [Ara Zobayan, flightOriginOnAccidentDay, John Wayne Airport, Orange County, California]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: flightOriginOnAccidentDay Context triple: [Ara Zobayan, flightOriginOnAccidentDay, John Wayne Airport, Orange County, California]
-
A.
originAirportAtTimeOfCrash
chosen
Indicates the airport from which an aircraft departed at the specific time when the crash occurred.
-
B.
hasOriginAirport
Indicates that something, typically a flight or journey, departs from or is associated with a specific origin airport.
-
C.
hasTypicalOriginAirport
Indicates that an entity, such as a flight route or airline service, is commonly or usually associated with a particular origin airport from which it typically departs.
-
D.
accidentAirport
Indicates the airport at which an accident involving the subject entity occurred.
-
E.
destinationAirportAtTimeOfCrash
Indicates the airport that was the intended destination of a flight at the time the crash 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_69ef5386c3e08190bfe33aa326e1f72b |
completed | April 27, 2026, 12:16 p.m. |
| NER | Named-entity recognition | batch_69f62f850d148190929315018576b731 |
completed | May 2, 2026, 5:08 p.m. |
| PD | Predicate disambiguation | batch_69f62c1762f881908c25e8f70ecd5041 |
completed | May 2, 2026, 4:53 p.m. |
Created at: April 27, 2026, 1:33 p.m.