Triple

T3306572
Position Surface form Disambiguated ID Type / Status
Subject Koichi Tanaka E69464 entity
Predicate givenName P17 FINISHED
Object Koichi
Koichi is a Japanese given name commonly used for males and borne by various notable figures in fields such as science, politics, and entertainment.
 E385381 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: Koichi | Statement: [Koichi Tanaka, givenName, Koichi]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Koichi
Context triple: [Koichi Tanaka, givenName, Koichi]
  • A. Shinya
    Shinya is a Japanese given name commonly used for males.
  • B. Kiichi
    Kiichi is a Japanese given name commonly used for males.
  • C. Kenjirō
    Kenjirō is a Japanese masculine given name that can be written with various kanji combinations and is borne by multiple notable individuals in fields such as sports, arts, and entertainment.
  • D. Kenkichi
    Kenkichi is a Japanese masculine given name that can be written with various kanji combinations and has been borne by numerous notable figures in fields such as sports, politics, and the arts.
  • E. Kentarō
    Kentarō is a Japanese given name commonly used for males, often associated with traditional or strong-sounding name combinations.
  • 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: Koichi
Triple: [Koichi Tanaka, givenName, Koichi]
Generated description
Koichi is a Japanese given name commonly used for males and borne by various notable figures in fields such as science, politics, and entertainment.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Koichi
Target entity description: Koichi is a Japanese given name commonly used for males and borne by various notable figures in fields such as science, politics, and entertainment.
  • A. Shinya
    Shinya is a Japanese given name commonly used for males.
  • B. Kiichi
    Kiichi is a Japanese given name commonly used for males.
  • C. Kenjirō
    Kenjirō is a Japanese masculine given name that can be written with various kanji combinations and is borne by multiple notable individuals in fields such as sports, arts, and entertainment.
  • D. Kenkichi
    Kenkichi is a Japanese masculine given name that can be written with various kanji combinations and has been borne by numerous notable figures in fields such as sports, politics, and the arts.
  • E. Kentarō
    Kentarō is a Japanese given name commonly used for males, often associated with traditional or strong-sounding name combinations.
  • 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_69ad859f218081909458d2cebbf57565 completed March 8, 2026, 2:20 p.m.
NER Named-entity recognition batch_69adb0caa0988190872fc7648e64571f completed March 8, 2026, 5:24 p.m.
NED1 Entity disambiguation (via context triple) batch_69b4e4cd6cd08190ba6d379c8dc48723 completed March 14, 2026, 4:32 a.m.
NEDg Description generation batch_69b4e594fe2081909623aac85f517f92 completed March 14, 2026, 4:35 a.m.
NED2 Entity disambiguation (via description) batch_69b4e60d2224819081b7a95592f1fd74 completed March 14, 2026, 4:37 a.m.
Created at: March 8, 2026, 3:11 p.m.