Triple
T6566477
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chungcheong region |
E153918
|
entity |
| Predicate | hasMajorCity |
P316
|
FINISHED |
| Object | Sejong City |
E570246
|
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: Sejong City | Statement: [Chungcheong region, hasMajorCity, Sejong City]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sejong City Context triple: [Chungcheong region, hasMajorCity, Sejong City]
-
A.
Sejong City
chosen
Sejong City is South Korea’s planned administrative capital, designed to house numerous government ministries and ease congestion in Seoul.
-
B.
Uijeongbu
Uijeongbu is a city in South Korea known as a suburban hub north of Seoul, featuring residential districts, commercial centers, and a history of hosting U.S. military bases.
-
C.
Daejeon
Daejeon is a major city in central South Korea known as a hub for science, technology, and research institutions.
-
D.
Yongin
Yongin is a rapidly growing city in the Seoul Capital Area of South Korea, known for attractions like Everland Resort and the Korean Folk Village.
-
E.
Dongducheon
Dongducheon is a city in northern South Korea known for its proximity to the Demilitarized Zone and the presence of U.S. military bases.
- 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_69c6880cb35881909b763eb0125236b9 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6ae5381e88190b44dc4440efdd8ae |
completed | March 27, 2026, 4:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8d66fdb1c81909ca125e5918b0997 |
completed | March 29, 2026, 7:36 a.m. |
Created at: March 27, 2026, 1:52 p.m.