Triple
T6688078
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Daejeon Station |
E152149
|
entity |
| Predicate | connectsToCity |
P4245
|
FINISHED |
| Object | Jeonju |
E607545
|
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: Jeonju | Statement: [Daejeon Station, connectsToCity, Jeonju]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jeonju Context triple: [Daejeon Station, connectsToCity, Jeonju]
-
A.
Jeonju
chosen
Jeonju is a historic city in southwestern South Korea known for its well-preserved Hanok Village, rich culinary traditions, and cultural heritage.
-
B.
Yeoju
Yeoju is a city in South Korea known for its rich historical heritage, including royal tombs and ceramics, and its scenic riverside landscapes.
-
C.
Chungju
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.
-
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.
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_69c687f9977c819097e7f5ada4fe522e |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6b14feb28819097bc157df8a2f96e |
completed | March 27, 2026, 4:33 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cb135210188190b61482e2d5747783 |
completed | March 31, 2026, 12:20 a.m. |
Created at: March 27, 2026, 2:04 p.m.