Triple
T22295990
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gangwon Province |
E551121
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Yeongwol County |
—
|
NE NERFINISHED |
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: Yeongwol County | Statement: [Gangwon Province, contains, Yeongwol County]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Yeongwol County Context triple: [Gangwon Province, contains, Yeongwol County]
-
A.
Yeongwol County
chosen
Yeongwol County is a rural county in Gangwon Province, South Korea, known for its scenic river valleys, historical sites, and cultural heritage.
-
B.
Yongwon County
Yongwon County is an administrative county located within South Pyongan Province in central North Korea.
-
C.
Seongju County
Seongju County is a rural administrative region in southeastern South Korea known for its melon farming and traditional cultural heritage.
-
D.
Yeoncheon County
Yeoncheon County is a rural county in Gyeonggi Province, South Korea, known for its location near the Demilitarized Zone (DMZ) and its historical military significance.
-
E.
Jeongseon County
Jeongseon County is a mountainous rural county in eastern South Korea known for its traditional culture, scenic landscapes, and coal-mining history.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e11e45fb848190a1b2ae21296e3a5f |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f1571fe76c8190a40b3679802a5475 |
completed | April 29, 2026, 12:56 a.m. |
Created at: April 16, 2026, 8:41 p.m.