Triple
T6853286
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Namdong District |
E158074
|
entity |
| Predicate | hasKoreanName |
P17869
|
FINISHED |
| Object | 남동구 |
E158074
|
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: 남동구 | Statement: [Namdong District, hasKoreanName, 남동구]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: 남동구 Context triple: [Namdong District, hasKoreanName, 남동구]
-
A.
Namdong District
chosen
Namdong District is a major administrative and commercial hub of Incheon, South Korea, known for housing the city hall and various industrial and residential areas.
-
B.
Yeongdeungpo District
Yeongdeungpo District is a major administrative and commercial area in southwestern Seoul, South Korea, known for its government institutions, business centers, and dense urban development.
-
C.
Dong-gu
Dong-gu is a district-level administrative area within the metropolitan city of Daejeon in South Korea.
-
D.
Dong-gu
Dong-gu is an administrative district of the metropolitan city of Ulsan in South Korea, known for its coastal location and industrial facilities.
-
E.
Eunpyeong-gu
Eunpyeong-gu is a district in northwestern Seoul, South Korea, known for its mix of urban residential areas and access to nearby mountains and temples.
- 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_69c6882fae988190864cbba788c5ebb4 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d84fffbc8190943ca7f3f03937e9 |
completed | March 27, 2026, 7:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c72fd79e508190b00e45f9cceb3e21 |
completed | March 28, 2026, 1:33 a.m. |
Created at: March 27, 2026, 2:20 p.m.