Triple
T7719863
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yuseong-gu |
E174979
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Yuseong Hot Springs |
E684592
|
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: Yuseong Hot Springs | Statement: [Yuseong-gu, contains, Yuseong Hot Springs]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Yuseong Hot Springs Context triple: [Yuseong-gu, contains, Yuseong Hot Springs]
-
A.
Yuseong Hot Springs
chosen
Yuseong Hot Springs is a historic and popular hot spring resort area in Daejeon, South Korea, known for its therapeutic mineral waters and public bath facilities.
-
B.
Daewangam Park
Daewangam Park is a coastal park in Ulsan, South Korea, known for its dramatic seaside cliffs, pine forest trails, and views of the Daewangam Rock formation.
-
C.
Haga Park
Haga Park is a historic royal park in the Stockholm area known for its landscaped grounds, cultural heritage sites, and recreational green spaces.
-
D.
Hwaseong
Hwaseong is a city in Gyeonggi Province, South Korea, known for its rapid industrial growth and proximity to major urban centers like Suwon and Seoul.
-
E.
Taejongdae Park
Taejongdae Park is a scenic coastal park in Busan, South Korea, famous for its dramatic seaside cliffs, lighthouse views, and walking trails overlooking the ocean.
- 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_69c6995d541c81909eaa646b1a8369a9 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c702eedc088190be645c029dfc462a |
completed | March 27, 2026, 10:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8be2b1c5c8190b80029ab6b8b9a3f |
completed | March 29, 2026, 5:52 a.m. |
Created at: March 27, 2026, 4:05 p.m.