Triple

T2929267
Position Surface form Disambiguated ID Type / Status
Subject Kumiko, the Treasure Hunter E78919 entity
Predicate mainCharacter P1183 FINISHED
Object Kumiko
Kumiko is the introspective Japanese woman at the center of the film "Kumiko, the Treasure Hunter," whose obsession with a fictional movie treasure drives her on a quixotic journey to America.
 E316736 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: Kumiko | Statement: [Kumiko, the Treasure Hunter, mainCharacter, Kumiko]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Kumiko
Context triple: [Kumiko, the Treasure Hunter, mainCharacter, Kumiko]
  • A. Totsuko
    Totsuko is the former abbreviated name of Tokyo Tsushin Kogyo, the Japanese company that later became Sony.
  • B. Michiko
    Michiko is the former Empress of Japan and the wife of Emperor Emeritus Akihito, known for being the first commoner to marry into the Japanese imperial family.
  • C. Shigeko
    Shigeko is a Japanese feminine given name that has been borne by various notable women, including members of the imperial family.
  • D. Kazuko
    Kazuko is a Japanese feminine given name commonly borne by women, including members of the imperial family.
  • E. Atsuko
    Atsuko is a Japanese feminine given name commonly borne by women and princesses in Japan, with meanings that vary depending on the kanji used.
  • 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: Kumiko
Triple: [Kumiko, the Treasure Hunter, mainCharacter, Kumiko]
Generated description
Kumiko is the introspective Japanese woman at the center of the film "Kumiko, the Treasure Hunter," whose obsession with a fictional movie treasure drives her on a quixotic journey to America.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Kumiko
Target entity description: Kumiko is the introspective Japanese woman at the center of the film "Kumiko, the Treasure Hunter," whose obsession with a fictional movie treasure drives her on a quixotic journey to America.
  • A. Totsuko
    Totsuko is the former abbreviated name of Tokyo Tsushin Kogyo, the Japanese company that later became Sony.
  • B. Michiko
    Michiko is the former Empress of Japan and the wife of Emperor Emeritus Akihito, known for being the first commoner to marry into the Japanese imperial family.
  • C. Shigeko
    Shigeko is a Japanese feminine given name that has been borne by various notable women, including members of the imperial family.
  • D. Kazuko
    Kazuko is a Japanese feminine given name commonly borne by women, including members of the imperial family.
  • E. Atsuko
    Atsuko is a Japanese feminine given name commonly borne by women and princesses in Japan, with meanings that vary depending on the kanji used.
  • 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_69ad8b0d40b481908bc2a5fa2e73c3fb completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69ad98002da4819098d6448eebcafad4 completed March 8, 2026, 3:38 p.m.
NED1 Entity disambiguation (via context triple) batch_69b108d249608190b386450c2ccab609 completed March 11, 2026, 6:16 a.m.
NEDg Description generation batch_69b10ce809d48190810236535c3316ad completed March 11, 2026, 6:34 a.m.
NED2 Entity disambiguation (via description) batch_69b10d22bbd48190b9878b4b0421c004 completed March 11, 2026, 6:35 a.m.
Created at: March 8, 2026, 2:55 p.m.