Triple

T4153346
Position Surface form Disambiguated ID Type / Status
Subject Sugamo Prison E89958 entity
Predicate notablePrisoner P15560 FINISHED
Object Yōsuke Matsuoka E306625 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: Yōsuke Matsuoka | Statement: [Sugamo Prison, notablePrisoner, Yōsuke Matsuoka]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Yōsuke Matsuoka
Context triple: [Sugamo Prison, notablePrisoner, Yōsuke Matsuoka]
  • A. Yosuke Matsuoka chosen
    Yosuke Matsuoka was a prominent Japanese diplomat and foreign minister of the early Shōwa era, known for his role in Japan’s expansionist foreign policy leading up to and during World War II.
  • B. Sankichi Takahashi
    Sankichi Takahashi was an admiral in the Imperial Japanese Navy who rose to its top wartime command as Commander-in-Chief of the Combined Fleet during World War II.
  • C. Saburō Kurusu
    Saburō Kurusu was a Japanese diplomat best known for his role in U.S.-Japan negotiations immediately before the attack on Pearl Harbor.
  • D. Sakon Yamamoto
    Sakon Yamamoto is a Japanese racing driver best known for his stint in Formula One during the mid-2000s with several backmarker teams.
  • E. Koichi Satō
    Koichi Satō is a prominent Japanese actor known for his versatile performances in film, television, and theater.
  • 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_69aed95a59a881909b26e70b42c6811a completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69af0277a910819085cde5df9a8110d8 completed March 9, 2026, 5:25 p.m.
NED1 Entity disambiguation (via context triple) batch_69c9660842888190a9d37a5fd1830ccd completed March 29, 2026, 5:48 p.m.
Created at: March 9, 2026, 3:44 p.m.