Triple

T10995041
Position Surface form Disambiguated ID Type / Status
Subject Ikujiro Nonaka E259841 entity
Predicate givenName P17 FINISHED
Object Ikujiro
Ikujiro is a Japanese organizational theorist best known for his work on knowledge management and the SECI model of knowledge creation.
 E926716 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: Ikujiro | Statement: [Ikujiro Nonaka, givenName, Ikujiro]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Ikujiro
Context triple: [Ikujiro Nonaka, givenName, Ikujiro]
  • A. Seikichi
    Seikichi is a Japanese given name commonly used for males.
  • B. Kinnosuke
    Kinnosuke is the given name of the renowned Japanese novelist Natsume Sōseki, a central figure in modern Japanese literature.
  • C. Ichirō
    Ichirō is a common Japanese masculine given name that can be written with various kanji and is often associated with first-born sons.
  • D. 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.
  • E. Tadahiko
    Tadahiko is a Japanese masculine given name used by various notable individuals in fields such as sports, arts, and academia.
  • 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: Ikujiro
Triple: [Ikujiro Nonaka, givenName, Ikujiro]
Generated description
Ikujiro is a Japanese organizational theorist best known for his work on knowledge management and the SECI model of knowledge creation.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Ikujiro
Target entity description: Ikujiro is a Japanese organizational theorist best known for his work on knowledge management and the SECI model of knowledge creation.
  • A. Seikichi
    Seikichi is a Japanese given name commonly used for males.
  • B. Kinnosuke
    Kinnosuke is the given name of the renowned Japanese novelist Natsume Sōseki, a central figure in modern Japanese literature.
  • C. Ichirō
    Ichirō is a common Japanese masculine given name that can be written with various kanji and is often associated with first-born sons.
  • D. 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.
  • E. Tadahiko
    Tadahiko is a Japanese masculine given name used by various notable individuals in fields such as sports, arts, and academia.
  • 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_69d6aa8a6a548190a750f944ccdc8064 completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d795d59ebc8190baff1f50bdc46c1b completed April 9, 2026, 12:04 p.m.
NED1 Entity disambiguation (via context triple) batch_69e5e890bb9c81908c316a6423e650e6 completed April 20, 2026, 8:49 a.m.
NEDg Description generation batch_69e5eeb38d588190b51b6c299bb717dd completed April 20, 2026, 9:15 a.m.
NED2 Entity disambiguation (via description) batch_69e5f19dd3348190b037e09c87b528e9 completed April 20, 2026, 9:27 a.m.
Created at: April 8, 2026, 9:24 p.m.