Triple

T5711796
Position Surface form Disambiguated ID Type / Status
Subject Michiko Kakutani E125926 entity
Predicate givenName P17 FINISHED
Object Michiko E4280 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: Michiko | Statement: [Michiko Kakutani, givenName, Michiko]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Michiko
Context triple: [Michiko Kakutani, givenName, Michiko]
  • A. Michiko chosen
    Michiko is the former Empress of Japan and the wife of Emperor Emeritus Akihito, known for being the first commoner to marry into the Japanese imperial family.
  • B. Sachiko
    Sachiko is a Japanese feminine given name that can be written with various kanji combinations, often conveying meanings related to happiness or child.
  • C. Totsuko
    Totsuko is the former abbreviated name of Tokyo Tsushin Kogyo, the Japanese company that later became Sony.
  • D. Kazuko
    Kazuko is a Japanese feminine given name commonly borne by women, including members of the imperial family.
  • E. Kumiko
    Kumiko is the introspective Japanese woman at the center of the film "Kumiko, the Treasure Hunter," whose obsession with a fictional movie treasure drives her on a quixotic journey to America.
  • 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_69c05a72181081909209a38c3ff7460b completed March 22, 2026, 9:09 p.m.
Created at: March 22, 2026, 3:46 p.m.