Triple
T3164176
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hangzhou |
E66170
|
entity |
| Predicate | sisterCity |
P1072
|
FINISHED |
| Object | Gyeongju |
E241549
|
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: Gyeongju | Statement: [Hangzhou, sisterCity, Gyeongju]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gyeongju Context triple: [Hangzhou, sisterCity, Gyeongju]
-
A.
Gyeongju
chosen
Gyeongju is a historic city in South Korea famed for its rich cultural heritage and numerous archaeological sites from the ancient Silla Kingdom.
-
B.
Gimhae
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.
-
C.
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.
-
D.
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.
-
E.
Daegu
Daegu is a major metropolitan city in southeastern South Korea known for its textile industry, electronics manufacturing, and cultural festivals.
- 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_69ad85850c1481908a9e9c6242238de2 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada61ba98881909106951c8ceeb959 |
completed | March 8, 2026, 4:38 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bd567a48b881908e4bfec70943eb60 |
completed | March 20, 2026, 2:15 p.m. |
Created at: March 8, 2026, 3:06 p.m.