Triple
T12057595
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 손기정 |
E287081
|
entity |
| Predicate | placeOfDeath |
P21
|
FINISHED |
| Object |
대한민국 서울특별시
대한민국 서울특별시는 대한민국의 수도이자 정치·경제·문화의 중심지인 대도시이다.
|
E960900
|
NE FINISHED |
How this triple was built (4 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: [손기정, placeOfDeath, 대한민국 서울특별시]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: 대한민국 서울특별시 Context triple: [손기정, placeOfDeath, 대한민국 서울특별시]
-
A.
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.
-
B.
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.
-
C.
Seongnam, Gyeonggi-do, South Korea
Seongnam is a major satellite city southeast of Seoul in Gyeonggi Province, known as a key technology and business hub in South Korea.
-
D.
Seodaemun-gu, Seoul
Seodaemun-gu, Seoul is a central district in western Seoul, South Korea, known for its universities, historical sites, and vibrant urban neighborhoods.
-
E.
Yeongdeungpo District, Seoul
Yeongdeungpo District, Seoul is a major commercial and residential area in southwestern Seoul, known for its business centers, shopping complexes, and dense urban development.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: 대한민국 서울특별시 Triple: [손기정, placeOfDeath, 대한민국 서울특별시]
Generated description
대한민국 서울특별시는 대한민국의 수도이자 정치·경제·문화의 중심지인 대도시이다.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: 대한민국 서울특별시 Target entity description: 대한민국 서울특별시는 대한민국의 수도이자 정치·경제·문화의 중심지인 대도시이다.
-
A.
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.
-
B.
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.
-
C.
Seongnam, Gyeonggi-do, South Korea
Seongnam is a major satellite city southeast of Seoul in Gyeonggi Province, known as a key technology and business hub in South Korea.
-
D.
Seodaemun-gu, Seoul
Seodaemun-gu, Seoul is a central district in western Seoul, South Korea, known for its universities, historical sites, and vibrant urban neighborhoods.
-
E.
Yeongdeungpo District, Seoul
Yeongdeungpo District, Seoul is a major commercial and residential area in southwestern Seoul, known for its business centers, shopping complexes, and dense urban development.
- F. None of above. chosen
Provenance (5 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_69d6ab4780948190bdb9f7620c2ac27e |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d9043bf0ec8190a51ef2641808320c |
completed | April 10, 2026, 2:07 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f49dea043c8190a74ffb448bbae5d0 |
completed | May 1, 2026, 12:34 p.m. |
| NEDg | Description generation | batch_69f53d96c3f08190847ba49929b7628f |
completed | May 1, 2026, 11:56 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f56505c0b481909f9caaf338f73033 |
completed | May 2, 2026, 2:44 a.m. |
Created at: April 8, 2026, 9:47 p.m.