Triple
T10730291
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chungcheongbuk-do |
E253052
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object | Chungju |
E445080
|
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: Chungju | Statement: [Chungcheongbuk-do, hasCity, Chungju]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Chungju Context triple: [Chungcheongbuk-do, hasCity, Chungju]
-
A.
Chungju
chosen
Chungju is a city in North Chungcheong Province, South Korea, known for its agricultural surroundings, historical sites, and the Chungju Dam on the Namhan River.
-
B.
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.
-
C.
Jecheon
Jecheon is a city in North Chungcheong Province, South Korea, known as a regional transport hub surrounded by mountains and lakes.
-
D.
Jeonju
Jeonju is a historic city in southwestern South Korea known for its well-preserved Hanok Village, rich culinary traditions, and cultural heritage.
-
E.
Gunsan
Gunsan is a coastal city in North Jeolla Province, South Korea, known for its port, industrial facilities, and longstanding association with nearby military air operations.
- 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_69d6aa5d8be481909a43218b2bfdbe95 |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d70fca498881909b40163a138cfb98 |
completed | April 9, 2026, 2:32 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6eabf5ed88190b6de7b99b5ab590f |
completed | May 3, 2026, 6:27 a.m. |
Created at: April 8, 2026, 9:14 p.m.