Triple
T4121400
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Takamatsu, Kagawa Prefecture, Japan |
E92619
|
entity |
| Predicate | twinnedWith |
P1072
|
FINISHED |
| Object | Jeju City |
E453323
|
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: Jeju City | Statement: [Takamatsu, Kagawa Prefecture, Japan, twinnedWith, Jeju City]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jeju City Context triple: [Takamatsu, Kagawa Prefecture, Japan, twinnedWith, Jeju City]
-
A.
Jeju City
chosen
Jeju City is the capital and largest city of South Korea’s Jeju Island, known for its volcanic landscapes, tourism, and role as a regional transportation and cultural hub.
-
B.
Ulsan
Ulsan is a major industrial city in southeastern South Korea, known for its large automobile, shipbuilding, and petrochemical complexes.
-
C.
Busan
Busan is South Korea’s second-largest city and a major international port known for its bustling harbor, beaches, and coastal scenery.
-
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.
Uijeongbu
Uijeongbu is a city in South Korea known as a suburban hub north of Seoul, featuring residential districts, commercial centers, and a history of hosting U.S. military bases.
- 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_69aed9685f70819086932777aec8d959 |
completed | March 9, 2026, 2:30 p.m. |
| NER | Named-entity recognition | batch_69af020549dc8190a81a5dbbf70288c7 |
completed | March 9, 2026, 5:23 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be031d16a08190b84524b7153f7f85 |
completed | March 21, 2026, 2:31 a.m. |
Created at: March 9, 2026, 3:41 p.m.