Triple

T20095617
Position Surface form Disambiguated ID Type / Status
Subject Mikasa-no-miya Takahito Shinnō E496391 entity
Predicate spouse P13 FINISHED
Object Yuriko, Princess Mikasa NE NERFINISHED

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: Yuriko, Princess Mikasa | Statement: [Mikasa-no-miya Takahito Shinnō, spouse, Yuriko, Princess Mikasa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Yuriko, Princess Mikasa
Context triple: [Mikasa-no-miya Takahito Shinnō, spouse, Yuriko, Princess Mikasa]
  • A. Yuriko
    Yuriko is the given name of Japanese actress Rinko Kikuchi, known for her roles in films such as "Babel" and "Pacific Rim."
  • B. Mikasa
    Mikasa is a small city in Hokkaido, Japan, known for its coal mining history and rich fossil discoveries.
  • C. Mikasa chosen
    Mikasa is a Japanese imperial family name most prominently associated with Prince Mikasa and his descendants within the modern Japanese monarchy.
  • D. Takako
    Takako is a Japanese feminine given name borne by various notable figures in politics, arts, and entertainment.
  • E. Mai Shiranui
    Mai Shiranui is a popular and iconic kunoichi (female ninja) character from SNK’s fighting games, known for her revealing outfit, agile fighting style, and fiery fan-based attacks.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69da626eee3881909f3454986d4a6511 completed April 11, 2026, 3:02 p.m.
NER Named-entity recognition batch_69e6666cc02481908780a415b19c05a2 completed April 20, 2026, 5:46 p.m.
Created at: April 11, 2026, 11:24 p.m.