Triple
T7719335
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gunsan |
E174966
|
entity |
| Predicate | hasAirport |
P105
|
FINISHED |
| Object |
Gunsan Airport
Gunsan Airport is a regional airport in Gunsan, South Korea, serving both civilian flights and military operations.
|
E684556
|
NE FINISHED |
How this triple was built (4 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: Gunsan Airport | Statement: [Gunsan, hasAirport, Gunsan Airport]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gunsan Airport Context triple: [Gunsan, hasAirport, Gunsan Airport]
-
A.
Sacheon Airport
Sacheon Airport is a regional airport in South Korea serving the city of Jinju and the surrounding Gyeongsangnam-do area with domestic flights.
-
B.
Gwangju Airport
Gwangju Airport is a regional airport in Gwangju, South Korea, serving domestic flights and limited military operations.
-
C.
Ujae Airport
Ujae Airport is a small public airstrip serving the remote Ujae Atoll in the Marshall Islands, providing essential air transport for local residents and supplies.
-
D.
Gimhae International Airport
Gimhae International Airport is the main international airport serving the Busan metropolitan area in South Korea.
-
E.
Neryungri Airport
Neryungri Airport is a regional airport in the Sakha Republic of Russia that serves the town of Neryungri and its surrounding area.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Gunsan Airport Triple: [Gunsan, hasAirport, Gunsan Airport]
Generated description
Gunsan Airport is a regional airport in Gunsan, South Korea, serving both civilian flights and military operations.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Gunsan Airport Target entity description: Gunsan Airport is a regional airport in Gunsan, South Korea, serving both civilian flights and military operations.
-
A.
Sacheon Airport
Sacheon Airport is a regional airport in South Korea serving the city of Jinju and the surrounding Gyeongsangnam-do area with domestic flights.
-
B.
Gwangju Airport
Gwangju Airport is a regional airport in Gwangju, South Korea, serving domestic flights and limited military operations.
-
C.
Ujae Airport
Ujae Airport is a small public airstrip serving the remote Ujae Atoll in the Marshall Islands, providing essential air transport for local residents and supplies.
-
D.
Gimhae International Airport
Gimhae International Airport is the main international airport serving the Busan metropolitan area in South Korea.
-
E.
Neryungri Airport
Neryungri Airport is a regional airport in the Sakha Republic of Russia that serves the town of Neryungri and its surrounding area.
- F. None of above. chosen
Provenance (5 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_69c6995c463c8190a14458036249d419 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c702eedc088190be645c029dfc462a |
completed | March 27, 2026, 10:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8b513f7d481908d2ce64d9685289c |
completed | March 29, 2026, 5:13 a.m. |
| NEDg | Description generation | batch_69c8b6f7148081908f699bd5600b6c57 |
completed | March 29, 2026, 5:21 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c8b7590ac08190ae43036828235ca7 |
completed | March 29, 2026, 5:23 a.m. |
Created at: March 27, 2026, 4:05 p.m.