Triple
T17934098
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | OSN |
E448406
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Osan, Gyeonggi Province |
—
|
NE NERFINISHED |
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: Osan, Gyeonggi Province | Statement: [OSN, locatedNear, Osan, Gyeonggi Province]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Osan, Gyeonggi Province Context triple: [OSN, locatedNear, Osan, Gyeonggi Province]
-
A.
Paju, Gyeonggi Province
Paju, in Gyeonggi Province, is a South Korean border city near the Demilitarized Zone known for sites like the Dorasan area and its symbolic role in inter-Korean relations.
-
B.
Osan, South Korea
chosen
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.
-
C.
오산시
오산시 is a city in Gyeonggi Province, South Korea, known as a suburban industrial and residential area located south of Seoul.
-
D.
Gwangju, Gyeonggi
Gwangju, Gyeonggi is a city in South Korea known for its blend of suburban residential areas, light industry, and historical sites within the Seoul Capital Area.
-
E.
Ansan
Ansan is a coastal industrial city in South Korea known for its manufacturing base, multicultural population, and proximity to Seoul.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8b9f79d14819095540856928f0e25 |
completed | April 10, 2026, 8:51 a.m. |
| NER | Named-entity recognition | batch_69e4a5547b7881909dc41bb7dd34194f |
completed | April 19, 2026, 9:50 a.m. |
Created at: April 10, 2026, 10:21 a.m.