Triple
T19116366
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 오세훈 |
E467917
|
entity |
| Predicate | placeOfBirth |
P1
|
FINISHED |
| Object | 대한민국 서울특별시 |
—
|
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: 대한민국 서울특별시 | Statement: [오세훈, placeOfBirth, 대한민국 서울특별시]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: 대한민국 서울특별시 Context triple: [오세훈, placeOfBirth, 대한민국 서울특별시]
-
A.
대한민국 서울특별시
chosen
대한민국 서울특별시는 대한민국의 수도이자 정치·경제·문화의 중심지인 대도시이다.
-
B.
Jung-gu, Seoul
Jung-gu, Seoul is a central district of South Korea’s capital city, known for its major commercial areas, historic sites, and key government and business institutions.
-
C.
Suwon, South Korea
Suwon, South Korea is a major city just south of Seoul known for its high-tech industry and the UNESCO-listed Hwaseong Fortress.
-
D.
Seongbuk-gu, Seoul
Seongbuk-gu, Seoul is a northern district of South Korea’s capital city known for its mix of residential neighborhoods, cultural sites, and major educational institutions.
-
E.
Osan, South Korea
Osan is a city in Gyeonggi Province, South Korea, known for its proximity to Osan Air Base and its role as a transportation and commercial hub south of Seoul.
- 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_69d8dd06a26481908039e2a1bae8c597 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5e3984bf48190818fa2b01b75decb |
completed | April 20, 2026, 8:28 a.m. |
Created at: April 10, 2026, 12:05 p.m.