Triple

T208463
Position Surface form Disambiguated ID Type / Status
Subject Taro Kono E4660 entity
Predicate familyName P18 FINISHED
Object Kono
Kono is a Japanese surname most prominently associated with politician Taro Kono, a leading figure in contemporary Japanese politics.
 E33223 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: Kono | Statement: [Taro Kono, familyName, Kono]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Kono
Context triple: [Taro Kono, familyName, Kono]
  • A. Kaiyukan
    Kaiyukan is a large, world-renowned public aquarium in Osaka, Japan, famous for its massive central tank and immersive marine life exhibits.
  • B. Kato
    Kato is the nickname of Kato Svanidze, who was the first wife of Soviet leader Joseph Stalin.
  • C. Takatsuki
    Takatsuki is a city in northern Osaka Prefecture, Japan, known as a residential and commercial hub between Osaka and Kyoto.
  • D. Tora
    Tora is a popular nickname for the Hanshin Tigers, a professional Japanese baseball team based in the Kansai region.
  • E. Shinsen
    Shinsen is a neighborhood in Tokyo’s Shibuya ward known for its residential streets, local eateries, and proximity to the bustling Shibuya Station area.
  • 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: Kono
Triple: [Taro Kono, familyName, Kono]
Generated description
Kono is a Japanese surname most prominently associated with politician Taro Kono, a leading figure in contemporary Japanese politics.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Kono
Target entity description: Kono is a Japanese surname most prominently associated with politician Taro Kono, a leading figure in contemporary Japanese politics.
  • A. Kaiyukan
    Kaiyukan is a large, world-renowned public aquarium in Osaka, Japan, famous for its massive central tank and immersive marine life exhibits.
  • B. Kato
    Kato is the nickname of Kato Svanidze, who was the first wife of Soviet leader Joseph Stalin.
  • C. Takatsuki
    Takatsuki is a city in northern Osaka Prefecture, Japan, known as a residential and commercial hub between Osaka and Kyoto.
  • D. Tora
    Tora is a popular nickname for the Hanshin Tigers, a professional Japanese baseball team based in the Kansai region.
  • E. Shinsen
    Shinsen is a neighborhood in Tokyo’s Shibuya ward known for its residential streets, local eateries, and proximity to the bustling Shibuya Station area.
  • 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_69a25737567c81908f9c505300239181 completed Feb. 28, 2026, 2:47 a.m.
NER Named-entity recognition batch_69a25c071fac81908f706d1384281182 completed Feb. 28, 2026, 3:07 a.m.
NED1 Entity disambiguation (via context triple) batch_69a37952cbbc8190afd920510408fd31 completed Feb. 28, 2026, 11:25 p.m.
NEDg Description generation batch_69a379ec65a48190a379e35cc0867ac2 completed Feb. 28, 2026, 11:27 p.m.
NED2 Entity disambiguation (via description) batch_69a37a81393881908a2a6c345d6f89fa completed Feb. 28, 2026, 11:30 p.m.
Created at: Feb. 28, 2026, 2:51 a.m.