Triple
T14371879
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Uiryeong County |
E356376
|
entity |
| Predicate | romanization |
P2508
|
FINISHED |
| Object | Uiryŏng-gun |
E1095361
|
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: Uiryŏng-gun | Statement: [Uiryeong County, romanization, Uiryŏng-gun]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Uiryŏng-gun Context triple: [Uiryeong County, romanization, Uiryŏng-gun]
-
A.
Uiryeong-gun
chosen
Uiryeong-gun is a rural county in South Gyeongsang Province, South Korea, known for its agricultural landscape and small-town communities.
-
B.
Jeungpyeong-gun
Jeungpyeong-gun is a rural county in central South Korea known for its agricultural landscape and location within North Chungcheong Province.
-
C.
Yeongdong-gun
Yeongdong-gun is a rural county in North Chungcheong Province, South Korea, known for its grape cultivation and traditional agricultural landscape.
-
D.
Boeun-gun
Boeun-gun is a rural county in central South Korea known for its apple orchards, scenic mountains, and historic Beopjusa Temple in Songnisan National Park.
-
E.
Ungchon-myeon
Ungchon-myeon is a rural township-level administrative division located within Ulju County in Ulsan, South Korea.
- 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_69d8279163a081908aec45c0e3f1e02f |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de8fb2082c8190b42cc5f2bab4f574 |
completed | April 14, 2026, 7:04 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd551002948190aeb93d245e1449a7 |
completed | May 8, 2026, 3:14 a.m. |
Created at: April 10, 2026, 1:15 a.m.