Triple
T5575152
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jongno-gu |
E146300
|
entity |
| Predicate | partOf |
P40
|
FINISHED |
| Object | Sudogwon |
E227776
|
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: Sudogwon | Statement: [Jongno-gu, partOf, Sudogwon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sudogwon Context triple: [Jongno-gu, partOf, Sudogwon]
-
A.
Sudogwon
chosen
Sudogwon is the Seoul Capital Area of South Korea, encompassing Seoul, Incheon, and surrounding Gyeonggi Province as the country’s largest and most populous metropolitan region.
-
B.
Gwangalli
Gwangalli is a coastal neighborhood in Busan, South Korea, best known for its sandy beach, vibrant nightlife, and scenic views of the nearby Gwangan Bridge.
-
C.
Wiryeseong
Wiryeseong was the first capital city of the ancient Korean kingdom of Baekje, located in the Han River basin near present-day Seoul.
-
D.
Won-dong
Won-dong is a neighborhood (dong) within the city of Osan in Gyeonggi Province, South Korea.
-
E.
Soi-myeon
Soi-myeon is a rural township-level administrative area located within Eumseong County in North Chungcheong Province, South Korea.
- 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_69c008ffed108190a084602227af6157 |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c02067e8d8819090a006cb266da5fe |
completed | March 22, 2026, 5:01 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c059f3f6648190af2fd9a5e7cc125b |
completed | March 22, 2026, 9:07 p.m. |
Created at: March 22, 2026, 3:37 p.m.