Triple

T12261574
Position Surface form Disambiguated ID Type / Status
Subject Hōnen E292236 entity
Predicate teacherOf P48 FINISHED
Object Shōkū
Shōkū was a prominent Japanese Buddhist monk of the Kamakura period and a leading disciple of Hōnen who helped develop and spread Pure Land (Jōdo) teachings.
 E974608 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: Shōkū | Statement: [Hōnen, teacherOf, Shōkū]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Shōkū
Context triple: [Hōnen, teacherOf, Shōkū]
  • A. Ōnamuchi
    Ōnamuchi is another name for Ōkuninushi, a major Shinto deity associated with nation-building, medicine, and good fortune in Japanese mythology.
  • B. Asago
    Asago is a city in northern Hyōgo Prefecture, Japan, known for its mountainous scenery, historic castle ruins, and hot spring resorts.
  • C. Hofu
    Hofu is a coastal city in western Honshu, Japan, known for its historic Hofu Tenmangu Shrine and industrial manufacturing base.
  • D. Fukutsu
    Fukutsu is a coastal city in southwestern Japan known for its beaches and location along the Genkai Sea in Fukuoka Prefecture.
  • E. Tocho
    Tocho is the common nickname for the Tokyo Metropolitan Government Building, a prominent skyscraper complex in Shinjuku that houses Tokyo’s metropolitan administration and offers popular observation decks.
  • 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: Shōkū
Triple: [Hōnen, teacherOf, Shōkū]
Generated description
Shōkū was a prominent Japanese Buddhist monk of the Kamakura period and a leading disciple of Hōnen who helped develop and spread Pure Land (Jōdo) teachings.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Shōkū
Target entity description: Shōkū was a prominent Japanese Buddhist monk of the Kamakura period and a leading disciple of Hōnen who helped develop and spread Pure Land (Jōdo) teachings.
  • A. Ōnamuchi
    Ōnamuchi is another name for Ōkuninushi, a major Shinto deity associated with nation-building, medicine, and good fortune in Japanese mythology.
  • B. Asago
    Asago is a city in northern Hyōgo Prefecture, Japan, known for its mountainous scenery, historic castle ruins, and hot spring resorts.
  • C. Hofu
    Hofu is a coastal city in western Honshu, Japan, known for its historic Hofu Tenmangu Shrine and industrial manufacturing base.
  • D. Fukutsu
    Fukutsu is a coastal city in southwestern Japan known for its beaches and location along the Genkai Sea in Fukuoka Prefecture.
  • E. Tocho
    Tocho is the common nickname for the Tokyo Metropolitan Government Building, a prominent skyscraper complex in Shinjuku that houses Tokyo’s metropolitan administration and offers popular observation decks.
  • 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_69d6ab6856488190b5d31178d5015f8e completed April 8, 2026, 7:24 p.m.
NER Named-entity recognition batch_69d91cd964ec81908241d2b9a96d1025 completed April 10, 2026, 3:52 p.m.
NED1 Entity disambiguation (via context triple) batch_69f61e65bc00819091e4fee3c3af6f4f completed May 2, 2026, 3:55 p.m.
NEDg Description generation batch_69f61f9386548190a749445a404db3a2 completed May 2, 2026, 4 p.m.
NED2 Entity disambiguation (via description) batch_69f6207f164c8190b663a50ee3c761d6 completed May 2, 2026, 4:04 p.m.
Created at: April 8, 2026, 9:52 p.m.