Triple

T18199032
Position Surface form Disambiguated ID Type / Status
Subject Gosei E435733 entity
Predicate followsInSequence P21351 FINISHED
Object Yonsei 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: Yonsei | Statement: [Gosei, followsInSequence, Yonsei]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Yonsei
Context triple: [Gosei, followsInSequence, Yonsei]
  • A. Yonsei chosen
    Yonsei are fourth-generation Japanese Americans, typically the great-grandchildren of Japanese immigrants to the United States.
  • B. Yonsei University
    Yonsei University is one of South Korea’s leading private research universities, renowned for its strong international programs and membership in prestigious global academic networks.
  • C. Chosun University
    Chosun University is a major private research university in South Korea known for its comprehensive academic programs and regional influence.
  • D. Sogang University
    Sogang University is a leading private research university in Seoul, South Korea, known for its strong humanities, social sciences, and business programs.
  • E. Korea University
    Korea University is a leading private research university in Seoul, South Korea, renowned for its comprehensive academic programs and status as one of the country’s top institutions.
  • 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_69d8b90dba6481908e119eb9aa4ca0cb completed April 10, 2026, 8:47 a.m.
NER Named-entity recognition batch_69e4e0d545f4819090285d1446bd3c27 completed April 19, 2026, 2:04 p.m.
Created at: April 10, 2026, 10:31 a.m.