Triple
T17811901
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cheongju |
E444728
|
entity |
| Predicate | romanization |
P2508
|
FINISHED |
| Object | Chongju |
—
|
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: Chongju | Statement: [Cheongju, romanization, Chongju]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Chongju Context triple: [Cheongju, romanization, Chongju]
-
A.
Chongju
chosen
Chongju is a city in northwestern North Korea known as an administrative and transportation center in North Pyongan Province.
-
B.
Gimcheon
Gimcheon is a city in North Gyeongsang Province, South Korea, known as a regional transportation hub and administrative center.
-
C.
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.
-
D.
Chuncheon
Chuncheon is a city in northeastern South Korea known for its lakes, surrounding mountains, and status as the capital of Gangwon Province.
-
E.
Kyongsong
Kyongsong is a coastal town and county-level city in northeastern North Korea known for its hot springs and location along the Sea of Japan (East Sea).
- 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_69d8b9f0de78819099395b14db75a8a6 |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e4887b5e50819098506f0b92d709b5 |
completed | April 19, 2026, 7:47 a.m. |
Created at: April 10, 2026, 10:14 a.m.