Triple
T10567314
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pusanjin-gu |
E249383
|
entity |
| Predicate | hasSubdivision |
P747
|
FINISHED |
| Object | Yangjeong-dong |
E263327
|
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: Yangjeong-dong | Statement: [Pusanjin-gu, hasSubdivision, Yangjeong-dong]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Yangjeong-dong Context triple: [Pusanjin-gu, hasSubdivision, Yangjeong-dong]
-
A.
Yangjeong-dong
chosen
Yangjeong-dong is a neighborhood (dong) located within Busanjin District in the city of Busan, South Korea.
-
B.
Cheongnyong-dong
Cheongnyong-dong is a neighborhood located within Geumjeong District in Busan, South Korea.
-
C.
Gocheon-dong
Gocheon-dong is a neighborhood (dong) that forms part of the city of Osan in Gyeonggi Province, South Korea.
-
D.
Yeocheon-dong
Yeocheon-dong is a neighborhood in Ulsan, South Korea, known for encompassing the expansive Ulsan Grand Park.
-
E.
Nonhyeon-dong
Nonhyeon-dong is a neighborhood in Seoul, South Korea, known for its mix of residential areas, commercial streets, and proximity to major business and shopping districts.
- 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_69d381c8bd708190acf3d275c908251e |
completed | April 6, 2026, 9:50 a.m. |
| NER | Named-entity recognition | batch_69d5272ef5848190b76d671ea2d26314 |
completed | April 7, 2026, 3:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e343cd76448190b0583cc15005ac9d |
completed | April 18, 2026, 8:41 a.m. |
Created at: April 6, 2026, 12:36 p.m.