Triple
T29293806
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Miyazaki–Tokyo Haneda |
E742760
|
entity |
| Predicate | airportOfDestinationRegion |
P40417
|
FINISHED |
| Object | Tokyo Metropolis |
—
|
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: Tokyo Metropolis | Statement: [Miyazaki–Tokyo Haneda, airportOfDestinationRegion, Tokyo Metropolis]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: airportOfDestinationRegion Context triple: [Miyazaki–Tokyo Haneda, airportOfDestinationRegion, Tokyo Metropolis]
-
A.
airportLocatedIn
Indicates that an airport is geographically situated within a specific administrative or territorial area.
-
B.
otherMajorAirportInRegion
Indicates that the subject airport is a different major airport located within the same geographic region as the object airport.
-
C.
airportLocatedWithin
chosen
Indicates that an airport is geographically situated inside the boundaries of a specified area or region.
-
D.
hasRegionalAirport
Indicates that a place or region possesses or is served by a regional airport.
-
E.
airportServesAs
Indicates that an airport functions in a particular role or capacity (such as primary, secondary, or hub) for a specified area, organization, or service.
- 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_69f0912323c48190b9a24ef8cf359225 |
completed | April 28, 2026, 10:51 a.m. |
| NER | Named-entity recognition | batch_69f6bbf6e33c819086e5176d64e7a614 |
completed | May 3, 2026, 3:07 a.m. |
| PD | Predicate disambiguation | batch_69f6ba6b1e6c8190adf9d6a257e0b744 |
completed | May 3, 2026, 3 a.m. |
Created at: April 28, 2026, 1:04 p.m.