Triple

T4977476
Position Surface form Disambiguated ID Type / Status
Subject Honeycomb (Dalgona) challenge E111802 entity
Predicate notableSceneForCharacter P7326 FINISHED
Object Kang Sae-byeok E116287 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: Kang Sae-byeok | Statement: [Honeycomb (Dalgona) challenge, notableSceneForCharacter, Kang Sae-byeok]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kang Sae-byeok
Context triple: [Honeycomb (Dalgona) challenge, notableSceneForCharacter, 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. Kwak Sin-ae
    Kwak Sin-ae is a South Korean film producer best known for producing the Academy Award–winning film "Parasite."
  • D. Heo Jeong
    Heo Jeong was a South Korean politician who served as prime minister and played a significant role in the country’s early post-war democratic politics.
  • E. Cha Jeong-in
    Cha Jeong-in is a South Korean academic who serves as the president of Pusan National University.
  • 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_69bd441adc208190b70a033a0741d01e completed March 20, 2026, 12:56 p.m.
NER Named-entity recognition batch_69bd77c9fc7c8190b165a5cfd5889ba8 completed March 20, 2026, 4:37 p.m.
NED1 Entity disambiguation (via context triple) batch_69beb0e1132881908fd6a540551e6318 completed March 21, 2026, 2:53 p.m.
Created at: March 20, 2026, 1:33 p.m.