Triple
T15765234
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | NGO |
E382202
|
entity |
| Predicate | associatedAirportName_ja |
P107376
|
FINISHED |
| Object | 中部国際空港 |
—
|
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: 中部国際空港 | Statement: [NGO, associatedAirportName_ja, 中部国際空港]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: associatedAirportName_ja Context triple: [NGO, associatedAirportName_ja, 中部国際空港]
-
A.
associatedAirportLocalName
chosen
Indicates the local or native-language name of the airport that is associated with the given entity.
-
B.
associatedAirport
Indicates a relationship where an entity is linked or connected to a specific airport, typically as its relevant or corresponding airport.
-
C.
airportOfficialName
Indicates the officially designated full name of an airport as recognized by authorities or governing bodies.
-
D.
airportLinkName
Indicates the name assigned to a transportation link or connection associated with an airport.
-
E.
associatedWithAirportName
Indicates a relationship where an entity is linked or connected to a specific airport by its name.
- 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_69d86da09a10819082fe9797b23e4664 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e050b8154881908afe5191e6424f15 |
completed | April 16, 2026, 3 a.m. |
| PD | Predicate disambiguation | batch_69e00531e7ac8190a4190cce4f7fab4c |
completed | April 15, 2026, 9:37 p.m. |
Created at: April 10, 2026, 4:47 a.m.