Triple

T16504530
Position Surface form Disambiguated ID Type / Status
Subject Yuki Satō (name) E400888 entity
Predicate hasNotableBearer P458 FINISHED
Object Yuki Satō (voice actor)
Yuki Satō is a Japanese voice actor known for performing character roles in anime and related media.
E1217473 NE FINISHED

How this triple was built (4 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: Yuki Satō (voice actor) | Statement: [Yuki Satō (name), hasNotableBearer, Yuki Satō (voice actor)]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Yuki Satō (voice actor)
Context triple: [Yuki Satō (name), hasNotableBearer, Yuki Satō (voice actor)]
  • A. Kawakami Yū (voice actress)
    Kawakami Yū is a Japanese voice actress known for her roles in anime, video games, and related media.
  • B. Masataka Kōno (voice actor)
    Masataka Kōno is a Japanese voice actor known for his work in anime and related media.
  • C. Kawakami Yūichi (voice actor)
    Kawakami Yūichi is a Japanese voice actor known for his roles in anime, video games, and related media.
  • D. Yuki Nagasato
    Yuki Nagasato is a Japanese professional footballer and World Cup–winning forward renowned for her prolific international career and success in top women’s leagues in Japan, Germany, and the United States.
  • E. Masayuki Yui
    Masayuki Yui is a Japanese actor known for his role in Akira Kurosawa’s epic film "Ran" and for his work in both cinema and television.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Yuki Satō (voice actor)
Triple: [Yuki Satō (name), hasNotableBearer, Yuki Satō (voice actor)]
Generated description
Yuki Satō is a Japanese voice actor known for performing character roles in anime and related media.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Yuki Satō (voice actor)
Target entity description: Yuki Satō is a Japanese voice actor known for performing character roles in anime and related media.
  • A. Kawakami Yū (voice actress)
    Kawakami Yū is a Japanese voice actress known for her roles in anime, video games, and related media.
  • B. Masataka Kōno (voice actor)
    Masataka Kōno is a Japanese voice actor known for his work in anime and related media.
  • C. Kawakami Yūichi (voice actor)
    Kawakami Yūichi is a Japanese voice actor known for his roles in anime, video games, and related media.
  • D. Yuki Nagasato
    Yuki Nagasato is a Japanese professional footballer and World Cup–winning forward renowned for her prolific international career and success in top women’s leagues in Japan, Germany, and the United States.
  • E. Masayuki Yui
    Masayuki Yui is a Japanese actor known for his role in Akira Kurosawa’s epic film "Ran" and for his work in both cinema and television.
  • F. None of above. chosen

Provenance (5 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_69d88381f6148190819958a038be990e completed April 10, 2026, 4:58 a.m.
NER Named-entity recognition batch_69e32e5100e48190a623d6ee2fefb87e completed April 18, 2026, 7:10 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0058305e308190a22cbd03daec53aa completed May 10, 2026, 10:04 a.m.
NEDg Description generation batch_6a005c0840608190bf3fa7a0501e9e8a completed May 10, 2026, 10:20 a.m.
NED2 Entity disambiguation (via description) batch_6a005c9d1a20819091a14490577d51ba completed May 10, 2026, 10:23 a.m.
Created at: April 10, 2026, 5:14 a.m.