Triple
T1978739
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | United States Forces Korea |
E42975
|
entity |
| Predicate | headquartersLocation |
P62
|
FINISHED |
| Object | Pyeongtaek |
E42974
|
NE 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: Pyeongtaek | Statement: [United States Forces Korea, headquartersLocation, Pyeongtaek]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pyeongtaek Context triple: [United States Forces Korea, headquartersLocation, Pyeongtaek]
-
A.
Pyeongtaek
chosen
Pyeongtaek is a South Korean city in Gyeonggi Province known for its major U.S. and UN military presence, including large bases such as Camp Humphreys.
-
B.
Anseong
Anseong is a city in Gyeonggi Province, South Korea, known for its traditional culture, agricultural heritage, and annual Baudeogi Festival.
-
C.
Daejeon
Daejeon is a major city in central South Korea known as a hub for science, technology, and research institutions.
-
D.
Daegu
Daegu is a major metropolitan city in southeastern South Korea known for its textile industry, electronics manufacturing, and cultural festivals.
-
E.
Dangjin
Dangjin is a coastal city in South Chungcheong Province, South Korea, known for its heavy industry, steel production, and port facilities on the Yellow Sea.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69a8871289048190b00b0d7744b7b2b1 |
completed | March 4, 2026, 7:25 p.m. |
| NER | Named-entity recognition | batch_69abb43011188190b6a41c004e9e4802 |
completed | March 7, 2026, 5:14 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b503cbee8481908f31124d8bc7fe9c |
completed | March 14, 2026, 6:44 a.m. |
Created at: March 4, 2026, 7:36 p.m.