Triple

T2919877
Position Surface form Disambiguated ID Type / Status
Subject Tokyo Vice E78692 entity
Predicate castMember P1668 FINISHED
Object Hideaki Ito
Hideaki Ito is a Japanese actor known for his roles in both film and television, including crime dramas and action series.
 E511139 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: Hideaki Ito | Statement: [Tokyo Vice, castMember, Hideaki Ito]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Hideaki Ito
Context triple: [Tokyo Vice, castMember, Hideaki Ito]
  • A. Makoto Yamashita
    Makoto Yamashita is a Japanese politician serving as the governor of Nara Prefecture.
  • B. Tatsuhiko Kawashima
    Tatsuhiko Kawashima is a Japanese academic and former professor best known as the father of Princess Kiko of the Japanese Imperial Family.
  • C. Hiromori Hayashi
    Hiromori Hayashi was a Japanese court musician of the Meiji era best known for arranging and formalizing the melody of Japan’s national anthem, "Kimigayo."
  • D. Koichi Tanaka
    Koichi Tanaka is a Japanese engineer and Nobel Prize–winning chemist renowned for his pioneering work in mass spectrometry, particularly soft laser desorption ionization.
  • E. Eiichi Kono
    Eiichi Kono is a Japanese type designer best known for his work on the digital revival and refinement of the iconic Johnston typeface used across the London Underground.
  • 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: Hideaki Ito
Triple: [Tokyo Vice, castMember, Hideaki Ito]
Generated description
Hideaki Ito is a Japanese actor known for his roles in both film and television, including crime dramas and action series.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Hideaki Ito
Target entity description: Hideaki Ito is a Japanese actor known for his roles in both film and television, including crime dramas and action series.
  • A. Makoto Yamashita
    Makoto Yamashita is a Japanese politician serving as the governor of Nara Prefecture.
  • B. Tatsuhiko Kawashima
    Tatsuhiko Kawashima is a Japanese academic and former professor best known as the father of Princess Kiko of the Japanese Imperial Family.
  • C. Hiromori Hayashi
    Hiromori Hayashi was a Japanese court musician of the Meiji era best known for arranging and formalizing the melody of Japan’s national anthem, "Kimigayo."
  • D. Koichi Tanaka
    Koichi Tanaka is a Japanese engineer and Nobel Prize–winning chemist renowned for his pioneering work in mass spectrometry, particularly soft laser desorption ionization.
  • E. Eiichi Kono
    Eiichi Kono is a Japanese type designer best known for his work on the digital revival and refinement of the iconic Johnston typeface used across the London Underground.
  • 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_69ad8b0c2ad081909ff87050ae542bb9 completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69ad96a53f8c8190b188d549f1161e84 completed March 8, 2026, 3:32 p.m.
NED1 Entity disambiguation (via context triple) batch_69bf185542508190ad71b753bda5d1a3 completed March 21, 2026, 10:14 p.m.
NEDg Description generation batch_69bf18e85d1c819090dfb9642d9f7a19 completed March 21, 2026, 10:17 p.m.
NED2 Entity disambiguation (via description) batch_69bf193c4e8881909357a21c234b9bfa completed March 21, 2026, 10:18 p.m.
Created at: March 8, 2026, 2:54 p.m.