Triple
T1978683
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pyeongtaek |
E42974
|
entity |
| Predicate | hasRomanization |
P2508
|
FINISHED |
| Object | Pyeongtaek-si |
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-si | Statement: [Pyeongtaek, hasRomanization, Pyeongtaek-si]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pyeongtaek-si Context triple: [Pyeongtaek, hasRomanization, Pyeongtaek-si]
-
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.
Daejeon
Daejeon is a major city in central South Korea known as a hub for science, technology, and research institutions.
-
C.
Daegu
Daegu is a major metropolitan city in southeastern South Korea known for its textile industry, electronics manufacturing, and cultural festivals.
-
D.
Jincheon County
Jincheon County is a rural administrative region in North Chungcheong Province, South Korea, known for its agricultural production and growing role as a logistics and industrial hub.
-
E.
Ulsan
Ulsan is a major industrial city in southeastern South Korea, known for its large automobile, shipbuilding, and petrochemical complexes.
- 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_69b4e4b980888190a7df10662789f61e |
completed | March 14, 2026, 4:31 a.m. |
Created at: March 4, 2026, 7:36 p.m.