Triple
T3956882
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Amagasaki |
E85802
|
entity |
| Predicate | hasSisterCity |
P919
|
FINISHED |
| Object | Gimhae |
E304192
|
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: Gimhae | Statement: [Amagasaki, hasSisterCity, Gimhae]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gimhae Context triple: [Amagasaki, hasSisterCity, Gimhae]
-
A.
Gimhae
chosen
Gimhae is a city in South Gyeongsang Province, South Korea, known for its historical significance as the birthplace of the ancient Gaya confederacy and its proximity to the metropolitan city of Busan.
-
B.
Gyeongju
Gyeongju is a historic city in South Korea famed for its rich cultural heritage and numerous archaeological sites from the ancient Silla Kingdom.
-
C.
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.
-
D.
Yeoju
Yeoju is a city in South Korea known for its rich historical heritage, including royal tombs and ceramics, and its scenic riverside landscapes.
-
E.
Hwaseong
Hwaseong is a city in Gyeonggi Province, South Korea, known for its rapid industrial growth and proximity to major urban centers like Suwon and Seoul.
- 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_69aed93a96908190bcbdbfa718f155bd |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aef95bbb9c8190bd64c5b7ea2f341a |
completed | March 9, 2026, 4:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bd678c2c088190a67867248f89c46a |
completed | March 20, 2026, 3:28 p.m. |
Created at: March 9, 2026, 3:31 p.m.