Triple
T16852769
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Koo In-hwoi |
E409714
|
entity |
| Predicate | placeOfBirth |
P1
|
FINISHED |
| Object | Jinju |
E510642
|
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: Jinju | Statement: [Koo In-hwoi, placeOfBirth, Jinju]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jinju Context triple: [Koo In-hwoi, placeOfBirth, Jinju]
-
A.
Seogwipo
Seogwipo is a coastal city on South Korea’s Jeju Island known for its waterfalls, volcanic landscapes, and popular tourist attractions.
-
B.
Jinju-si
chosen
Jinju-si is a city in South Gyeongsang Province, South Korea, known for its historic Jinju Fortress and the annual Namgang Yudeung (Lantern) Festival.
-
C.
Gyeongseong
Gyeongseong was the Japanese colonial-era name for Seoul, which served as the administrative and political center of Korea under Japanese rule.
-
D.
Hongseong
Hongseong is a town in South Korea that serves as the administrative capital of South Chungcheong Province.
-
E.
Neryungri
Neryungri is a major coal-mining and industrial city in southeastern Siberia, Russia, known as one of the key urban centers of the Sakha Republic (Yakutia).
- 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_69d88395e6c88190b22730f335107c14 |
completed | April 10, 2026, 4:59 a.m. |
| NER | Named-entity recognition | batch_69e3b37abadc81909d02d329403497d6 |
completed | April 18, 2026, 4:38 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00bb216fac81909d401c6b9911d1e0 |
completed | May 10, 2026, 5:06 p.m. |
Created at: April 10, 2026, 5:24 a.m.