Triple
T13292924
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chiayi City |
E316602
|
entity |
| Predicate | hasSisterCity |
P919
|
FINISHED |
| Object | Gumi, South Korea |
E336613
|
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: Gumi, South Korea | Statement: [Chiayi City, hasSisterCity, Gumi, South Korea]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gumi, South Korea Context triple: [Chiayi City, hasSisterCity, Gumi, South Korea]
-
A.
Gumi, South Korea
chosen
Gumi, South Korea is an industrial city in North Gyeongsang Province known as a major electronics manufacturing hub and home to several large technology companies.
-
B.
Gunsan, South Korea
Gunsan, South Korea is a coastal industrial city in North Jeolla Province known for its port, manufacturing facilities, and role as a regional transportation hub.
-
C.
Bupyong, South Korea
Bupyong, South Korea is an industrial district in Incheon known for its major automotive manufacturing facilities and dense urban development.
-
D.
Osan, South Korea
Osan is a city in Gyeonggi Province, South Korea, known for its proximity to Osan Air Base and its role as a transportation and commercial hub south of Seoul.
-
E.
Ulsan, South Korea
Ulsan, South Korea is a major industrial port city known as a global hub for automobile and ship manufacturing.
- 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_69d806b349908190a9a61dd9323bf153 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d99078bcf0819083195fb556bcacb2 |
completed | April 11, 2026, 12:06 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f716d6dc988190ab7183089113237f |
completed | May 3, 2026, 9:35 a.m. |
Created at: April 9, 2026, 9:27 p.m.