Triple
T7026832
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | South Jeolla region |
E162969
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object | Suncheon |
E620722
|
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: Suncheon | Statement: [South Jeolla region, hasCity, Suncheon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Suncheon Context triple: [South Jeolla region, hasCity, Suncheon]
-
A.
Suncheon
chosen
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.
-
B.
Gunsan
Gunsan is a coastal city in North Jeolla Province, South Korea, known for its port, industrial facilities, and longstanding association with nearby military air operations.
-
C.
Tongyeong
Tongyeong is a coastal city in South Gyeongsang Province, South Korea, known for its scenic archipelago, seafood, and maritime history.
-
D.
Icheon
Icheon is a South Korean city renowned for its traditional ceramics and hot spring resorts.
-
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_69d4d14df7888190aadd3f506b6b4cd2 |
completed | April 7, 2026, 9:41 a.m. |
Created at: March 27, 2026, 2:35 p.m.