Triple
T15858346
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gyeryong |
E384517
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Daejeon |
E28250
|
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: Daejeon | Statement: [Gyeryong, locatedNear, Daejeon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Daejeon Context triple: [Gyeryong, locatedNear, Daejeon]
-
A.
Daejeon
chosen
Daejeon is a major city in central South Korea known as a hub for science, technology, and research institutions.
-
B.
Daegu
Daegu is a major metropolitan city in southeastern South Korea known for its textile industry, electronics manufacturing, and cultural festivals.
-
C.
Gwangju
Gwangju is a major metropolitan city in southwestern South Korea known for its rich cultural heritage and pivotal role in the country’s pro-democracy movement.
-
D.
Ulsan
Ulsan is a major industrial city in southeastern South Korea, known for its large automobile, shipbuilding, and petrochemical complexes.
-
E.
Incheon
Incheon is a major port city in northwestern South Korea, known for its international airport and role as a key transportation and economic hub.
- 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_69d86da422088190aac39e32e6c68429 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e1555956ec8190b13602a177e7a2bb |
completed | April 16, 2026, 9:32 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffb03c8cb081908c18c7b2d143c4b4 |
completed | May 9, 2026, 10:07 p.m. |
Created at: April 10, 2026, 4:50 a.m.