Triple
T20257782
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sabi |
E498749
|
entity |
| Predicate | otherName |
P39
|
FINISHED |
| Object | Sabi-seong |
—
|
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: Sabi-seong | Statement: [Sabi, otherName, Sabi-seong]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sabi-seong Context triple: [Sabi, otherName, Sabi-seong]
-
A.
Cheong-seok
Cheong-seok is a young Buddhist monk character in the South Korean supernatural horror film "The 8th Night," who becomes involved in a struggle against an ancient evil.
-
B.
Gwangmyeong
Gwangmyeong is a city in South Korea known for its proximity to Seoul and attractions like the Gwangmyeong Cave, a former mine turned cultural and tourism complex.
-
C.
Hwaseong
Hwaseong is a city in Gyeonggi Province, South Korea, known for its rapid industrial growth and proximity to major urban centers like Suwon and Seoul.
-
D.
Sinchon
Sinchon is a vibrant university district in Seoul, South Korea, known for its dense concentration of colleges, youth culture, shopping, and nightlife.
-
E.
Sanggyeong
chosen
Sanggyeong was the principal royal capital city of the Balhae kingdom, serving as its political and cultural center in Northeast Asia.
- 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_69da6275fa6c8190952924930adee150 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e674c7296c819092860942de8f28d5 |
completed | April 20, 2026, 6:47 p.m. |
Created at: April 11, 2026, 11:41 p.m.