Triple
T6981178
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Daewangam Park |
E161848
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Dong-gu, Ulsan |
E182488
|
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: Dong-gu, Ulsan | Statement: [Daewangam Park, locatedIn, Dong-gu, Ulsan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dong-gu, Ulsan Context triple: [Daewangam Park, locatedIn, Dong-gu, Ulsan]
-
A.
Dong-gu, Busan
Dong-gu, Busan is a central district of Busan, South Korea, known for its major transportation hubs, dense urban neighborhoods, and coastal views.
-
B.
Dong-gu
Dong-gu is a district-level administrative area within the metropolitan city of Daejeon in South Korea.
-
C.
Dong-gu
chosen
Dong-gu is an administrative district of the metropolitan city of Ulsan in South Korea, known for its coastal location and industrial facilities.
-
D.
Nam-gu, Ulsan
Nam-gu, Ulsan is a coastal district in the metropolitan city of Ulsan, South Korea, known for its industrial facilities and maritime heritage.
-
E.
Busan Jung District
Busan Jung District is a central urban district of Busan, South Korea, known for its historic downtown area, bustling commercial streets, and major shopping 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_69c68855dc0481909b4c7e9e9ed273db |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6db6d3f3c8190b0121f7934440c34 |
completed | March 27, 2026, 7:33 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c78839d37c8190a127c01e2aeb71ee |
completed | March 28, 2026, 7:50 a.m. |
Created at: March 27, 2026, 2:31 p.m.