Triple
T11815298
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | YSSY |
E280983
|
entity |
| Predicate | associatedAirportPrimaryUse |
P70143
|
FINISHED |
| Object | civil aviation |
—
|
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: civil aviation | Statement: [YSSY, associatedAirportPrimaryUse, civil aviation]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: associatedAirportPrimaryUse Context triple: [YSSY, associatedAirportPrimaryUse, civil aviation]
-
A.
airportUse
Indicates that an airport is used or utilized by a particular entity, such as an airline, organization, or service.
-
B.
previousPrimaryAirportFor
Indicates that one airport was formerly the main or primary airport serving a particular location or entity before being replaced by another.
-
C.
associatedAirportServes
Indicates that a given airport provides service to, or is used by, the associated entity (such as a city, region, or facility).
-
D.
airportSpecialization
chosen
Indicates that an airport is specialized or designated for a particular primary function, service type, or category of operations.
-
E.
associatedAirport
Indicates a relationship where an entity is linked or connected to a specific airport, typically as its relevant or corresponding airport.
- 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_69d6ab26aae88190b2489efcb2a24234 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d8a658f918819092c2db05fe2ab0ce |
completed | April 10, 2026, 7:27 a.m. |
| PD | Predicate disambiguation | batch_69d8a24e9a088190aff7932d1ff93dbf |
completed | April 10, 2026, 7:10 a.m. |
Created at: April 8, 2026, 9:42 p.m.