Triple
T5565969
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Seoul Special City |
E145879
|
entity |
| Predicate | formerName |
P65
|
FINISHED |
| Object | Hanseong |
E514441
|
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: Hanseong | Statement: [Seoul Special City, formerName, Hanseong]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hanseong Context triple: [Seoul Special City, formerName, Hanseong]
-
A.
Hanseong
chosen
Hanseong was the historical name for Seoul when it served as the capital of the Joseon Dynasty in Korea.
-
B.
Gwangmyeong
Gwangmyeong is a city in South Korea known for its proximity to Seoul and attractions like the Gwangmyeong Cave, a former mine turned cultural and tourism complex.
-
C.
Kaesong
Kaesong is a historic city in present-day North Korea that served as the capital of the medieval Korean kingdom of Goryeo and remains known for its cultural heritage and traditional architecture.
-
D.
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.
-
E.
Dangjin
Dangjin is a coastal city in South Chungcheong Province, South Korea, known for its heavy industry, steel production, and port facilities on the Yellow Sea.
- 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_69c008fdae24819081aa002ad99cd966 |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c02034fc3081908920c52a19d462e1 |
completed | March 22, 2026, 5 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c04d125e808190b0360da35d514920 |
completed | March 22, 2026, 8:12 p.m. |
Created at: March 22, 2026, 3:36 p.m.