Triple
T22295969
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gangwon Province |
E551121
|
entity |
| Predicate | largestCity |
P235
|
FINISHED |
| Object | Wonju |
—
|
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: Wonju | Statement: [Gangwon Province, largestCity, Wonju]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wonju Context triple: [Gangwon Province, largestCity, Wonju]
-
A.
Wonju
chosen
Wonju is a city in South Korea’s Gangwon Province known historically as a strategic military site and today as a regional commercial and transportation hub.
-
B.
Yeongcheon
Yeongcheon is a city in southeastern South Korea known for its agricultural production and historical sites within North Gyeongsang Province.
-
C.
Sangju
Sangju is a city in southeastern South Korea known historically for agriculture, particularly rice and dried persimmons, and for its role as a regional transport hub.
-
D.
Namyangju
Namyangju is a city in South Korea known for its scenic natural landscapes, historical sites, and role as a suburban area within the Seoul metropolitan region.
-
E.
Jincheon
Jincheon is a county in North Chungcheong Province, South Korea, known for its agricultural production and growing role as a logistics and industrial hub.
- 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.