Triple

T10731005
Position Surface form Disambiguated ID Type / Status
Subject Ban Ki-moon E253072 entity
Predicate child P120 FINISHED
Object Ban Woo-hyun E253072 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: Ban Woo-hyun | Statement: [Ban Ki-moon, child, Ban Woo-hyun]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ban Woo-hyun
Context triple: [Ban Ki-moon, child, Ban Woo-hyun]
  • A. Ban Woo-hyun chosen
    Ban Woo-hyun is one of the children of former UN Secretary-General Ban Ki-moon and his wife Yoo Soon-taek.
  • B. Kim Swoo Geun
    Kim Swoo Geun was a prominent South Korean architect renowned for pioneering modern Korean architecture and shaping Seoul’s urban landscape in the mid-20th century.
  • C. Kim Je-hyuk
    Kim Je-hyuk is the naive yet kindhearted star baseball player who becomes an unlikely inmate protagonist in the South Korean television drama "Prison Playbook."
  • D. Yoon Je-moon
    Yoon Je-moon is a South Korean actor known for his versatile performances in both critically acclaimed films and television dramas.
  • E. Lee Dong-hwi
    Lee Dong-hwi is a South Korean actor known for his roles in popular films and television dramas, including the hit series "Reply 1988."
  • 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_69d6aa5d8be481909a43218b2bfdbe95 completed April 8, 2026, 7:19 p.m.
NER Named-entity recognition batch_69d70fcb1cd881909635def59ad5d19c completed April 9, 2026, 2:32 a.m.
NED1 Entity disambiguation (via context triple) batch_69de557c4ea4819090b0c6c2175e05ea completed April 14, 2026, 2:55 p.m.
Created at: April 8, 2026, 9:14 p.m.