Triple
T9160612
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Namcheon-dong |
E219810
|
entity |
| Predicate | partOf |
P40
|
FINISHED |
| Object | Suyeong-gu |
E698278
|
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: Suyeong-gu | Statement: [Namcheon-dong, partOf, Suyeong-gu]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Suyeong-gu Context triple: [Namcheon-dong, partOf, Suyeong-gu]
-
A.
Suyeong-gu
chosen
Suyeong-gu is a coastal district in the city of Busan, South Korea, known for its urban neighborhoods and proximity to popular beaches.
-
B.
Yeonsu-gu
Yeonsu-gu is an administrative district of Incheon, South Korea, known for its coastal location, modern residential areas, and proximity to the Songdo International Business District.
-
C.
Gangseo-gu
Gangseo-gu is a western coastal district of Busan, South Korea, known for its industrial complexes, logistics hubs, and proximity to Gimhae International Airport.
-
D.
Geumjeong-gu
Geumjeong-gu is a district in Busan, South Korea, known for its residential areas, transportation hubs, and proximity to Geumjeongsan Mountain.
-
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_69ca83e3633c81908688a9fa2306ba99 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69ccaa2ac0508190b2f5c801c2c26d66 |
completed | April 1, 2026, 5:16 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1eaa8135081909225810ee5355bc3 |
completed | April 5, 2026, 4:52 a.m. |
Created at: March 30, 2026, 7:21 p.m.