Triple
T21538494
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gumi |
E531414
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Sangju |
—
|
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: Sangju | Statement: [Gumi, locatedNear, Sangju]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sangju Context triple: [Gumi, locatedNear, Sangju]
-
A.
Sangju
chosen
Sangju is a city in southeastern South Korea known historically for agriculture, particularly rice and dried persimmons, and for its role as a regional transport hub.
-
B.
Wonju
Wonju is a city in South Korea’s Gangwon Province known historically as a strategic military site and today as a regional commercial and transportation hub.
-
C.
Namyangju
Namyangju is a city in South Korea known for its scenic natural landscapes, historical sites, and role as a suburban area within the Seoul metropolitan region.
-
D.
Yeongcheon
Yeongcheon is a city in southeastern South Korea known for its agricultural production and historical sites within North Gyeongsang Province.
-
E.
Sunchon
Sunchon is an industrial city in western North Korea known for its chemical and coal industries.
- 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_69e0c45e5b8881908ac18fc2f493b114 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69ee9d10a2888190bc4e502a829c76a4 |
completed | April 26, 2026, 11:17 p.m. |
Created at: April 16, 2026, 6:27 p.m.