Triple
T23485843
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 안익태 |
E570529
|
entity |
| Predicate | burialPlace |
P196
|
FINISHED |
| Object | 대한민국 서울특별시 동작구 |
—
|
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 동작구 | Statement: [안익태, burialPlace, 대한민국 서울특별시 동작구]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: 대한민국 서울특별시 동작구 Context triple: [안익태, burialPlace, 대한민국 서울특별시 동작구]
-
A.
대한민국 서울특별시
대한민국 서울특별시는 대한민국의 수도이자 정치·경제·문화의 중심지인 대도시이다.
-
B.
Dongjak District, Seoul, South Korea
chosen
Dongjak District is a central residential and educational ward in southern Seoul, South Korea, known for its riverside parks, major universities, and convenient transport links.
-
C.
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.
-
D.
Gwacheon, Gyeonggi-do, South Korea
Gwacheon, in Gyeonggi-do, South Korea, is a planned city just south of Seoul known for its cultural institutions, government complexes, and proximity to major museums and parks.
-
E.
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.
- 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_69e245b0b01481908f636939bedd804c |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f1a7538a8c8190b7effcc39a3f9787 |
completed | April 29, 2026, 6:38 a.m. |
Created at: April 17, 2026, 6:03 p.m.