Triple
T9171901
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Oncheonjang Station |
E220100
|
entity |
| Predicate | district |
P2709
|
FINISHED |
| Object | Dongnae District |
E34836
|
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: Dongnae District | Statement: [Oncheonjang Station, district, Dongnae District]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dongnae District Context triple: [Oncheonjang Station, district, Dongnae District]
-
A.
Dongnae District
chosen
Dongnae District is a historic and central administrative district of Busan, South Korea, known for its hot springs and cultural heritage sites.
-
B.
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.
-
C.
Busanjin District
Busanjin District is a central urban district of Busan, South Korea, known as a major commercial and transportation hub of the city.
-
D.
Cijin District
Cijin District is a coastal district of Kaohsiung, Taiwan, known for its historic port, seafood markets, and popular seaside attractions.
-
E.
Suyeong District
Suyeong District is an urban coastal district in Busan, South Korea, known for its beaches, residential areas, and cultural attractions.
- 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_69ca83e467108190abcae6a33b3d4dad |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69ccaae38ee48190bf783477bc37913d |
completed | April 1, 2026, 5:19 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d228318de881909bbf4e68331bb586 |
completed | April 5, 2026, 9:15 a.m. |
Created at: March 30, 2026, 7:22 p.m.