Triple

T11325328
Position Surface form Disambiguated ID Type / Status
Subject Ryūnosuke E268197 entity
Predicate nameElement P27866 FINISHED
Object Ryū E334744 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: Ryū | Statement: [Ryūnosuke, nameElement, Ryū]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ryū
Context triple: [Ryūnosuke, nameElement, Ryū]
  • A. Ryūō
    Ryūō is a town in Shiga Prefecture, Japan, known for its location near Lake Biwa and its blend of rural landscapes with growing commercial development.
  • B. Ryojun
    Ryojun is the former Japanese name for Lüshunkou, a strategically important port city in northeastern China historically known for its military significance.
  • C. Yorii
    Yorii is a town in Saitama Prefecture, Japan, known as a regional residential and commuter hub connected to the greater Tokyo area.
  • D. Shinya chosen
    Shinya is a Japanese given name commonly used for males.
  • E. Takehiro
    Takehiro is a central character in Ryūnosuke Akutagawa’s short story “In a Grove,” whose ambiguous fate is revealed through conflicting eyewitness testimonies.
  • 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_69d6aacb1f0881908c84a349fd1be047 completed April 8, 2026, 7:21 p.m.
NER Named-entity recognition batch_69d7e9e2253881909518cad0f12ef612 completed April 9, 2026, 6:03 p.m.
NED1 Entity disambiguation (via context triple) batch_69e6e72931208190b91ef4be770c00d4 completed April 21, 2026, 2:55 a.m.
Created at: April 8, 2026, 9:32 p.m.