Triple
T23216831
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eunpyeong-gu |
E580767
|
entity |
| Predicate | hasHangulName |
P149035
|
FINISHED |
| Object | 은평구 |
—
|
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: 은평구 | Statement: [Eunpyeong-gu, hasHangulName, 은평구]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: 은평구 Context triple: [Eunpyeong-gu, hasHangulName, 은평구]
-
A.
성북구
성북구는 서울특별시 북동부에 위치한 주거·교육·문화 시설이 밀집한 자치구이다.
-
B.
Eunpyeong-gu
chosen
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.
-
C.
Seodaemun-gu
Seodaemun-gu is a central district in Seoul, South Korea, known for its major universities, historical sites, and vibrant urban neighborhoods.
-
D.
Dongdaemun-gu
Dongdaemun-gu is a central district in Seoul, South Korea, known for its major commercial areas, historic sites, and the iconic Dongdaemun Design Plaza.
-
E.
종로구
종로구 is a central district in Seoul, South Korea, known as the historic and political heart of the city, home to major palaces, government institutions, and cultural landmarks.
- 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_69e2460389408190be74f41d217799a9 |
completed | April 17, 2026, 2:38 p.m. |
| NER | Named-entity recognition | batch_69f19165949c81908e4d66a8a2b0a25a |
completed | April 29, 2026, 5:04 a.m. |
Created at: April 17, 2026, 4:08 p.m.