Triple
T16360368
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Incheon |
E397293
|
entity |
| Predicate | hasDistrict |
P459
|
FINISHED |
| Object | Bupyeong District |
E159653
|
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: Bupyeong District | Statement: [Incheon, hasDistrict, Bupyeong District]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bupyeong District Context triple: [Incheon, hasDistrict, Bupyeong District]
-
A.
Bupyeong District
chosen
Bupyeong District is a populous urban district of Incheon, South Korea, known as a major residential, commercial, and transportation hub in the metropolitan area.
-
B.
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.
-
C.
Suyeong District
Suyeong District is an urban coastal district in Busan, South Korea, known for its beaches, residential areas, and cultural attractions.
-
D.
Seongbuk District
Seongbuk District is a residential and educational borough in northern Seoul, South Korea, known for its universities, cultural sites, and traditional neighborhoods.
-
E.
Gwangjin District
Gwangjin District is an eastern borough of Seoul, South Korea, known for its universities, shopping areas, and location along the Han River.
- 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_69d87f2778dc8190aa95c7572db127e6 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e2fad241848190a9f32c7b050f20a5 |
completed | April 18, 2026, 3:30 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a006798cf488190a68cf7e57902924e |
completed | May 10, 2026, 11:10 a.m. |
Created at: April 10, 2026, 5:08 a.m.