Triple
T20861540
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kang Cheol |
E513629
|
entity |
| Predicate | relative |
P37
|
FINISHED |
| Object | Kang Sae-byeok |
—
|
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: Kang Sae-byeok | Statement: [Kang Cheol, relative, Kang Sae-byeok]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kang Sae-byeok Context triple: [Kang Cheol, relative, Kang Sae-byeok]
-
A.
Kang Sae-byeok
chosen
Kang Sae-byeok is a North Korean defector and pickpocket who becomes one of the central, emotionally resonant contestants in the deadly survival competition of the South Korean series "Squid Game."
-
B.
Jung Jang-seon
Jung Jang-seon is a South Korean politician serving as the mayor of the city of Pyeongtaek.
-
C.
Byeon Bong-seon
Byeon Bong-seon is a South Korean cinematographer known for his work on the sci-fi film "Space Sweepers."
-
D.
Dong Hee-seon
Dong Hee-seon is a South Korean screenwriter best known for her work on the hit fantasy-comedy film "Miss Granny."
-
E.
Koh Sang-ji
Koh Sang-ji is a notable individual bearing the Korean surname Koh, recognized enough to be specifically cited among its prominent bearers.
- 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_69e0b4f5b01081909452f654d2fc3f50 |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c3ad3d1c8190be2fe35a85f2447c |
completed | April 21, 2026, 12:24 a.m. |
Created at: April 16, 2026, 12:44 p.m.