Triple
T23682411
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Takuya Onishi |
E585063
|
entity |
| Predicate | positionAtAirline |
P153368
|
FINISHED |
| Object | co-pilot |
—
|
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: co-pilot | Statement: [Takuya Onishi, positionAtAirline, co-pilot]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: positionAtAirline Context triple: [Takuya Onishi, positionAtAirline, co-pilot]
-
A.
positionOnFlight
Indicates the specific seat or positional assignment that an entity has on a particular flight.
-
B.
hasRelativePositionAtAirport
Indicates that one entity has a specific spatial or positional relationship to another entity within the context or layout of an airport.
-
C.
airlineContext
Indicates a relationship, situation, or action that specifically occurs within or is constrained by an airline-related context (such as flights, carriers, or air travel operations).
-
D.
airportLineRole
Indicates the specific functional role or responsibility an entity has in relation to an airport transit line or route.
-
E.
locatedAtAirportCode
Indicates that an entity is situated at, associated with, or occurs at the airport identified by a specific airport code.
- F. None of above. chosen
Provenance (4 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_69e24901f7c08190909fd727632e823d |
completed | April 17, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69f1b4f93dd081909040ff117a82b87e |
completed | April 29, 2026, 7:36 a.m. |
| PD | Predicate disambiguation | batch_69f155d5265881908e43a9696b6a6d0f |
completed | April 29, 2026, 12:50 a.m. |
| PDg | Predicate description generation | batch_69f157cc43a881909ed2d8b0a09b5d73 |
completed | April 29, 2026, 12:58 a.m. |
Created at: April 17, 2026, 6:51 p.m.