Triple

T3107529
Position Surface form Disambiguated ID Type / Status
Subject Itō Sukeyuki E64868 entity
Predicate givenName P17 FINISHED
Object Sukeyuki E64868 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: Sukeyuki | Statement: [Itō Sukeyuki, givenName, Sukeyuki]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sukeyuki
Context triple: [Itō Sukeyuki, givenName, Sukeyuki]
  • A. Kunitachi
    Kunitachi is a suburban city in western Tokyo, Japan, known for its universities, tree-lined avenues, and residential character.
  • B. Shigeko
    Shigeko is a Japanese feminine given name that has been borne by various notable women, including members of the imperial family.
  • C. Yodo-dono
    Yodo-dono was a prominent Japanese noblewoman and political figure of the late Sengoku period, best known as Toyotomi Hideyoshi’s consort and the mother of his heir, Toyotomi Hideyori.
  • D. Yuriko
    Yuriko is the given name of Japanese actress Rinko Kikuchi, known for her roles in films such as "Babel" and "Pacific Rim."
  • E. Itō Sukeyuki chosen
    Itō Sukeyuki was a Japanese admiral who became prominent as a leading naval commander during Japan’s early modern wars and the country’s rise as a maritime power.
  • 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_69ad857eeaf48190b34ebfdaa7a264cf completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69ada29d4aa8819093287bc71370fc05 completed March 8, 2026, 4:23 p.m.
NED1 Entity disambiguation (via context triple) batch_69b261eb2e708190b192574d3f5862e6 completed March 12, 2026, 6:49 a.m.
Created at: March 8, 2026, 3:04 p.m.