Triple
T9293878
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Osan |
E223589
|
entity |
| Predicate | hangulName |
P17869
|
FINISHED |
| Object | 오산시 |
E363256
|
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: 오산시 | Statement: [Osan, hangulName, 오산시]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: 오산시 Context triple: [Osan, hangulName, 오산시]
-
A.
오산시
chosen
오산시 is a city in Gyeonggi Province, South Korea, known as a suburban industrial and residential area located south of Seoul.
-
B.
수원시
수원시는 경기도 중남부에 위치한 광역시급 기초자치단체로, 행정·산업·교육의 중심지이자 수원 화성으로 유명한 도시이다.
-
C.
Ansan
Ansan is a coastal industrial city in South Korea known for its manufacturing base, multicultural population, and proximity to Seoul.
-
D.
Gwacheon
Gwacheon is a small city in South Korea known for hosting major government offices, cultural institutions, and the Seoul Grand Park complex.
-
E.
Pyeongtaek
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.
- 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_69ca8423edb08190bc0c91287a484768 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd089a2ed0819086b2e4219ff2453e |
completed | April 1, 2026, 11:59 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d0b241251c81909aa4e8bcf5cd9c2e |
completed | April 4, 2026, 6:40 a.m. |
Created at: March 30, 2026, 7:35 p.m.