Triple

T3810597
Position Surface form Disambiguated ID Type / Status
Subject Takako Doi E93123 entity
Predicate givenName P17 FINISHED
Object Takako
Takako is a Japanese feminine given name borne by various notable figures in politics, arts, and entertainment.
 E393709 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: Takako | Statement: [Takako Doi, givenName, Takako]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Takako
Context triple: [Takako Doi, givenName, Takako]
  • A. Naoko
    Naoko is a central, emotionally fragile character in Haruki Murakami’s story "Norwegian Wood," whose complex relationship with the protagonist explores themes of love, loss, and mental illness.
  • B. 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.
  • C. Yuriko
    Yuriko is the given name of Japanese actress Rinko Kikuchi, known for her roles in films such as "Babel" and "Pacific Rim."
  • D. Totsuko
    Totsuko is the former abbreviated name of Tokyo Tsushin Kogyo, the Japanese company that later became Sony.
  • E. Haruko
    Haruko, better known as Empress Shōken, was the consort of Emperor Meiji and a prominent Japanese empress noted for her support of modernization and social welfare.
  • 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: Takako
Triple: [Takako Doi, givenName, Takako]
Generated description
Takako is a Japanese feminine given name borne by various notable figures in politics, arts, and entertainment.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Takako
Target entity description: Takako is a Japanese feminine given name borne by various notable figures in politics, arts, and entertainment.
  • A. Naoko
    Naoko is a central, emotionally fragile character in Haruki Murakami’s story "Norwegian Wood," whose complex relationship with the protagonist explores themes of love, loss, and mental illness.
  • B. 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.
  • C. Yuriko
    Yuriko is the given name of Japanese actress Rinko Kikuchi, known for her roles in films such as "Babel" and "Pacific Rim."
  • D. Totsuko
    Totsuko is the former abbreviated name of Tokyo Tsushin Kogyo, the Japanese company that later became Sony.
  • E. Haruko
    Haruko, better known as Empress Shōken, was the consort of Emperor Meiji and a prominent Japanese empress noted for her support of modernization and social welfare.
  • 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_69aed96a60088190ab1df8390fffc935 completed March 9, 2026, 2:30 p.m.
NER Named-entity recognition batch_69aee80e178081908cff71223bbf6c43 completed March 9, 2026, 3:32 p.m.
NED1 Entity disambiguation (via context triple) batch_69b503f3590c8190b18e2e9dfd84cbcd completed March 14, 2026, 6:45 a.m.
NEDg Description generation batch_69b507cfee048190a41ad30f4ceaf6c8 completed March 14, 2026, 7:01 a.m.
NED2 Entity disambiguation (via description) batch_69b50857e9ec8190bb03f13c4573b779 completed March 14, 2026, 7:03 a.m.
Created at: March 9, 2026, 3:16 p.m.