Triple
T7026831
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | South Jeolla region |
E162969
|
entity |
| Predicate | hasLargestCity |
P235
|
FINISHED |
| Object | Yeosu |
E624731
|
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: Yeosu | Statement: [South Jeolla region, hasLargestCity, Yeosu]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Yeosu Context triple: [South Jeolla region, hasLargestCity, Yeosu]
-
A.
Yeosu
chosen
Yeosu is a coastal city in South Jeolla Province, South Korea, known for its scenic archipelago, maritime industry, and role as host of the 2012 World Expo.
-
B.
Mokpo
Mokpo is a coastal city in South Jeolla Province, South Korea, known as a regional transportation hub and gateway to numerous nearby islands.
-
C.
Suncheon
Suncheon is a city in South Jeolla Province, South Korea, known for its ecological attractions such as the Suncheon Bay Wetland Reserve and its role as a regional administrative and cultural center.
-
D.
Kurseong
Kurseong is a small hill town in the Darjeeling district of northern West Bengal, India, known for its tea gardens, cool climate, and views of the Eastern Himalayas.
-
E.
Yeoju
Yeoju is a city in South Korea known for its rich historical heritage, including royal tombs and ceramics, and its scenic riverside landscapes.
- 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_69c6885b26248190a857541e3d10e299 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6e1fd6ab48190865271e16e8ff669 |
completed | March 27, 2026, 8:01 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7cbc1b76081909094a9b2f215e58d |
completed | March 28, 2026, 12:38 p.m. |
Created at: March 27, 2026, 2:35 p.m.