Triple
T20748131
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jinju-si |
E510642
|
entity |
| Predicate | hasRiver |
P165
|
FINISHED |
| Object | Namgang (Nam River) |
—
|
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: Namgang (Nam River) | Statement: [Jinju-si, hasRiver, Namgang (Nam River)]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Namgang (Nam River) Context triple: [Jinju-si, hasRiver, Namgang (Nam River)]
-
A.
Namgang River
chosen
The Namgang River is a scenic waterway in South Korea known for flowing through Jinju and hosting the famous Jinju Namgang Yudeung (Lantern) Festival.
-
B.
Suyeong River
The Suyeong River is a major waterway flowing through Busan, South Korea, known for its scenic riverside areas and proximity to key cultural and urban landmarks.
-
C.
Yeong River
The Yeong River is a river in South Korea that feeds into the larger Nakdong River system.
-
D.
Nam-gu
Nam-gu is a central urban district of Daegu in South Korea, known for its residential neighborhoods, commercial areas, and educational institutions.
-
E.
Nam-gu
Nam-gu is a central administrative district of the metropolitan city of Ulsan in South Korea, known for its residential areas, commercial centers, and proximity to major industrial complexes.
- 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_69e0b4c845e88190b4c5f3ae79291182 |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c226fbf881909794eff3ee9e206b |
completed | April 21, 2026, 12:17 a.m. |
Created at: April 16, 2026, 12:33 p.m.