Triple
T6774686
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Onsan National Industrial Complex |
E155126
|
entity |
| Predicate | locatedInAdministrativeTerritory |
P40
|
FINISHED |
| Object | Nam-gu, Ulsan |
E605538
|
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: Nam-gu, Ulsan | Statement: [Onsan National Industrial Complex, locatedInAdministrativeTerritory, Nam-gu, Ulsan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nam-gu, Ulsan Context triple: [Onsan National Industrial Complex, locatedInAdministrativeTerritory, Nam-gu, Ulsan]
-
A.
Nam-gu, Ulsan
chosen
Nam-gu, Ulsan is a coastal district in the metropolitan city of Ulsan, South Korea, known for its industrial facilities and maritime heritage.
-
B.
Mokneung
Mokneung is one of the royal burial sites from Korea’s Joseon Dynasty, forming part of the UNESCO-listed Royal Tombs complex.
-
C.
Icheon
Icheon is a South Korean city renowned for its traditional ceramics and hot spring resorts.
-
D.
Siheung
Siheung is a coastal city in northwestern South Korea known for its industrial complexes, wetlands, and proximity to Seoul.
-
E.
Dangjin
Dangjin is a coastal city in South Chungcheong Province, South Korea, known for its heavy industry, steel production, and port facilities on the Yellow Sea.
- 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_69c68812ef7c819099369f51febb725c |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d24ddaf08190baffbff991eeb458 |
completed | March 27, 2026, 6:54 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c712ca48d88190b9f47b23264d4264 |
completed | March 27, 2026, 11:29 p.m. |
Created at: March 27, 2026, 2:13 p.m.