Triple
T18631463
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kunsan Air Base |
E455426
|
entity |
| Predicate | serves |
P98
|
FINISHED |
| Object | city of Gunsan |
—
|
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: city of Gunsan | Statement: [Kunsan Air Base, serves, city of Gunsan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: city of Gunsan Context triple: [Kunsan Air Base, serves, city of Gunsan]
-
A.
Gyeryong city
Gyeryong city is a South Korean city in South Chungcheong Province known for its proximity to the sacred mountain Gyeryongsan and its role as a major military and administrative center.
-
B.
Tancheon
Tancheon is a river in South Korea that flows through the city of Seongnam and serves as a key urban waterway and recreational area.
-
C.
Gimcheon
Gimcheon is a city in North Gyeongsang Province, South Korea, known as a regional transportation hub and administrative center.
-
D.
Gunsan, South Korea
chosen
Gunsan, South Korea is a coastal industrial city in North Jeolla Province known for its port, manufacturing facilities, and role as a regional transportation hub.
-
E.
Jincheon
Jincheon is a county in North Chungcheong Province, South Korea, known for its agricultural production and growing role as a logistics and industrial hub.
- 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_69d8d38cc7948190a55ea64e5638994e |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e54fc4c5648190b771e9b080e98c15 |
completed | April 19, 2026, 9:57 p.m. |
Created at: April 10, 2026, 11:46 a.m.