Triple

T5711797
Position Surface form Disambiguated ID Type / Status
Subject Michiko Kakutani E125926 entity
Predicate familyName P18 FINISHED
Object Kakutani E30873 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: Kakutani | Statement: [Michiko Kakutani, familyName, Kakutani]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kakutani
Context triple: [Michiko Kakutani, familyName, Kakutani]
  • A. Shizuo Kakutani chosen
    Shizuo Kakutani was a Japanese-American mathematician renowned for his contributions to functional analysis and probability theory, including the formulation of the Kakutani fixed-point theorem.
  • B. Takaichi
    Takaichi is a Japanese surname most prominently associated with conservative politician Sanae Takaichi.
  • C. Kiyotake Kawaguchi
    Kiyotake Kawaguchi was an Imperial Japanese Army general best known for leading Japanese forces in the Guadalcanal campaign during World War II.
  • D. Kiyokawa
    Kiyokawa is a small rural village in Kanagawa Prefecture, Japan, known for its mountainous scenery and outdoor recreation.
  • E. Tadahiko
    Tadahiko is a Japanese masculine given name used by various notable individuals in fields such as sports, arts, and academia.
  • 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_69c0082d6fe48190b777fb383769e5c8 completed March 22, 2026, 3:18 p.m.
NER Named-entity recognition batch_69c024b386a08190bd2738d93861edc2 completed March 22, 2026, 5:19 p.m.
NED1 Entity disambiguation (via context triple) batch_69c07dedffd481909fafd916190b016f completed March 22, 2026, 11:40 p.m.
Created at: March 22, 2026, 3:46 p.m.