Triple
T20796153
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Daegu |
E511913
|
entity |
| Predicate | hasDistrict |
P459
|
FINISHED |
| Object | Dalseo District |
—
|
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: Dalseo District | Statement: [Daegu, hasDistrict, Dalseo District]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dalseo District Context triple: [Daegu, hasDistrict, Dalseo District]
-
A.
Dalseo District
chosen
Dalseo District is an administrative district (gu) in the southwestern part of Daegu, South Korea, known for its residential areas, commercial centers, and urban parks.
-
B.
Dongnae District
Dongnae District is a historic and central administrative district of Busan, South Korea, known for its hot springs and cultural heritage sites.
-
C.
Suyeong District
Suyeong District is an urban coastal district in Busan, South Korea, known for its beaches, residential areas, and cultural attractions.
-
D.
Bupyeong District
Bupyeong District is a populous urban district of Incheon, South Korea, known as a major residential, commercial, and transportation hub in the metropolitan area.
-
E.
Gwangsan District
Gwangsan District is one of the administrative districts of Gwangju, South Korea, known for its mix of urban development and transportation hubs including Gwangju Songjeong station.
- 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_69e0b4cb83948190bd57bec21d78ed53 |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c2ad6f0481909e0bab7119f10f9c |
completed | April 21, 2026, 12:19 a.m. |
Created at: April 16, 2026, 12:39 p.m.