Triple
T22295982
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gangwon Province |
E551121
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Chuncheon |
—
|
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: Chuncheon | Statement: [Gangwon Province, contains, Chuncheon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Chuncheon Context triple: [Gangwon Province, contains, Chuncheon]
-
A.
Chuncheon
chosen
Chuncheon is a city in northeastern South Korea known for its lakes, surrounding mountains, and status as the capital of Gangwon Province.
-
B.
Sunchon
Sunchon is an industrial city in western North Korea known for its chemical and coal industries.
-
C.
Jecheon
Jecheon is a city in North Chungcheong Province, South Korea, known as a regional transport hub surrounded by mountains and lakes.
-
D.
Cheongju
Cheongju is a major city in central South Korea that serves as the capital of North Chungcheong Province and an important regional administrative, educational, and transportation hub.
-
E.
Gimcheon
Gimcheon is a city in North Gyeongsang Province, South Korea, known as a regional transportation hub and administrative center.
- 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.