Triple

T1934114
Position Surface form Disambiguated ID Type / Status
Subject Tokyo Tsushin Kogyo E41406 entity
Predicate shortName P43 FINISHED
Object Totsuko
Totsuko is the former abbreviated name of Tokyo Tsushin Kogyo, the Japanese company that later became Sony.
 E229178 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: Totsuko | Statement: [Tokyo Tsushin Kogyo, shortName, Totsuko]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Totsuko
Context triple: [Tokyo Tsushin Kogyo, shortName, Totsuko]
  • A. Shigeko
    Shigeko is a Japanese feminine given name that has been borne by various notable women, including members of the imperial family.
  • B. Yuriko
    Yuriko is the given name of Japanese actress Rinko Kikuchi, known for her roles in films such as "Babel" and "Pacific Rim."
  • C. Kazuko
    Kazuko is a Japanese feminine given name commonly borne by women, including members of the imperial family.
  • D. 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.
  • 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: Totsuko
Triple: [Tokyo Tsushin Kogyo, shortName, Totsuko]
Generated description
Totsuko is the former abbreviated name of Tokyo Tsushin Kogyo, the Japanese company that later became Sony.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Totsuko
Target entity description: Totsuko is the former abbreviated name of Tokyo Tsushin Kogyo, the Japanese company that later became Sony.
  • A. Shigeko
    Shigeko is a Japanese feminine given name that has been borne by various notable women, including members of the imperial family.
  • B. Yuriko
    Yuriko is the given name of Japanese actress Rinko Kikuchi, known for her roles in films such as "Babel" and "Pacific Rim."
  • C. Kazuko
    Kazuko is a Japanese feminine given name commonly borne by women, including members of the imperial family.
  • D. 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.
  • 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_69a88649b24c819080047f26b6db2ded completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69abb29b51408190afb2f918814e68c7 completed March 7, 2026, 5:07 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae1fd173b881909fbd454fc9d7fabb completed March 9, 2026, 1:18 a.m.
NEDg Description generation batch_69ae2078f5bc81909e4226e4f4188e87 completed March 9, 2026, 1:20 a.m.
NED2 Entity disambiguation (via description) batch_69ae2121a43481908ea6eef3d4e06407 completed March 9, 2026, 1:23 a.m.
Created at: March 4, 2026, 7:35 p.m.