Triple
T20669754
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 진주 |
E507987
|
entity |
| Predicate | hasAirport |
P105
|
FINISHED |
| Object | Sacheon 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: Sacheon Airport | Statement: [진주, hasAirport, Sacheon Airport]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sacheon Airport Context triple: [진주, hasAirport, Sacheon Airport]
-
A.
Sacheon Airport
chosen
Sacheon Airport is a regional airport in South Korea serving the city of Jinju and the surrounding Gyeongsangnam-do area with domestic flights.
-
B.
Gunsan Airport
Gunsan Airport is a regional airport in Gunsan, South Korea, serving both civilian flights and military operations.
-
C.
Cheongju International Airport
Cheongju International Airport is a major regional airport in central South Korea that serves both domestic and international flights for the city of Cheongju and the surrounding Chungcheong region.
-
D.
Pohang Airport
Pohang Airport is a regional airport in Pohang, South Korea, serving both civilian flights and military operations.
-
E.
Gimhae International Airport
Gimhae International Airport is the main international airport serving the Busan metropolitan area in South Korea.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b4c059bc81908ea762cd73ea4424 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6b5c735048190a01cb7692928d66e |
completed | April 20, 2026, 11:24 p.m. |
Created at: April 16, 2026, 11:44 a.m.