Triple
T6570308
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yeonsu District |
E155416
|
entity |
| Predicate | hasNeighborhood |
P40
|
FINISHED |
| Object | Dongchun-dong |
E639111
|
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: Dongchun-dong | Statement: [Yeonsu District, hasNeighborhood, Dongchun-dong]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dongchun-dong Context triple: [Yeonsu District, hasNeighborhood, Dongchun-dong]
-
A.
Cheonghak-dong
Cheonghak-dong is a neighborhood within the city of Osan in Gyeonggi Province, South Korea.
-
B.
Sogyeok-dong
Sogyeok-dong is a neighborhood in central Seoul, South Korea, known for its traditional Korean houses (hanok), art galleries, and proximity to historic palaces.
-
C.
Okryeon-dong
chosen
Okryeon-dong is a neighborhood located within Yeonsu District in Incheon, South Korea.
-
D.
Sinsa-dong
Sinsa-dong is a fashionable neighborhood in Seoul, South Korea, known for its trendy boutiques, cafes, and the popular Garosu-gil shopping street.
-
E.
Oryun-dong
Oryun-dong is a neighborhood (dong) located within Geumjeong District in Busan, South Korea.
- 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_69c688151254819080387f87deab8fa7 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6ae5791e881909d0b340aa63c6223 |
completed | March 27, 2026, 4:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7942437bc8190808e60d12b98dcf5 |
completed | March 28, 2026, 8:41 a.m. |
Created at: March 27, 2026, 1:53 p.m.