Triple
T28632982
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fukuoka–Tokyo Haneda |
E724694
|
entity |
| Predicate | airportAtOtherEnd |
P58803
|
FINISHED |
| Object | Haneda Airport |
—
|
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: Haneda Airport | Statement: [Fukuoka–Tokyo Haneda, airportAtOtherEnd, Haneda Airport]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: airportAtOtherEnd Context triple: [Fukuoka–Tokyo Haneda, airportAtOtherEnd, Haneda Airport]
-
A.
airportStation
Indicates a location functions as an airport facility where air transport operations occur.
-
B.
destinationAirportAlternativeName
Indicates that an airport serves as an alternative or secondary destination for another airport, typically used when the primary destination is unavailable or unsuitable.
-
C.
otherAirportOfCity
Indicates that the subject airport is another airport serving the same city as the object airport.
-
D.
associatedAirport
chosen
Indicates a relationship where an entity is linked or connected to a specific airport, typically as its relevant or corresponding airport.
-
E.
destinationAirportName
Indicates the name of the airport that serves as the destination in a travel or flight-related relationship.
- 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_69f01d8328c48190bc0e5f9b9b848582 |
completed | April 28, 2026, 2:37 a.m. |
| NER | Named-entity recognition | batch_69f6b49436b0819094e21603054d05d4 |
completed | May 3, 2026, 2:36 a.m. |
| PD | Predicate disambiguation | batch_69f6b3a5fd8481909433e923c5e24e55 |
completed | May 3, 2026, 2:32 a.m. |
Created at: April 28, 2026, 4:38 a.m.