Triple

T23404880
Position Surface form Disambiguated ID Type / Status
Subject 오영수 E559607 entity
Predicate name P16 FINISHED
Object 오영수 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: 오영수 | Statement: [오영수, name, 오영수]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: 오영수
Context triple: [오영수, name, 오영수]
  • A. 오영수 chosen
    오영수는 넷플릭스 드라마 「오징어 게임」에서의 노인 참가자 ‘오일남’ 역으로 세계적인 주목을 받은 대한민국의 배우이다.
  • B. Koo Dae-sung
    Koo Dae-sung is a South Korean former professional baseball pitcher known for his successful career in the KBO League, Nippon Professional Baseball, and Major League Baseball.
  • C. Cho Kuk
    Cho Kuk is a South Korean legal scholar and politician who briefly served as Minister of Justice and became a central figure in major national controversies over academic privilege and political reform.
  • D. Shim Sung-bo
    Shim Sung-bo is a South Korean film director and screenwriter best known for co-writing the acclaimed thriller "Memories of Murder" and directing the maritime drama "Sea Fog."
  • E. Kim Young-sam
    Kim Young-sam was a South Korean politician who served as the country’s president in the 1990s and is known for advancing democratic reforms and anti-corruption measures.
  • 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_69e24549610c8190a069d6411ce5f661 completed April 17, 2026, 2:35 p.m.
NER Named-entity recognition batch_69f1a4e27db88190b37375b38073291c completed April 29, 2026, 6:27 a.m.
Created at: April 17, 2026, 5:38 p.m.