Triple

T16186571
Position Surface form Disambiguated ID Type / Status
Subject Mariko Svanidze E392819 entity
Predicate givenName P17 FINISHED
Object Mariko E913821 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: Mariko | Statement: [Mariko Svanidze, givenName, Mariko]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mariko
Context triple: [Mariko Svanidze, givenName, Mariko]
  • A. Mariko chosen
    Mariko is a central female character in the 1980 television miniseries "Shogun," known for her complex role as a noblewoman navigating political intrigue and cultural conflict in feudal Japan.
  • B. Misako
    Misako is a key character in the Ninjago universe, known as an archaeologist and historian who is the mother of Lloyd Garmadon and the wife of Garmadon.
  • C. Masako
    Masako is the Empress of Japan, a former diplomat and Harvard-educated member of the Imperial House known for her international background and public role.
  • 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. Takako
    Takako is a Japanese feminine given name borne by various notable figures in politics, arts, and entertainment.
  • 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_69d87f1e49ac8190a311b54d32990576 completed April 10, 2026, 4:39 a.m.
NER Named-entity recognition batch_69e22061f47481909ededd5eed40f5a4 completed April 17, 2026, 11:58 a.m.
NED1 Entity disambiguation (via context triple) batch_6a006ecc2b4c8190ac9654ac826f198e completed May 10, 2026, 11:41 a.m.
Created at: April 10, 2026, 5:02 a.m.