Triple

T15650359
Position Surface form Disambiguated ID Type / Status
Subject Bansho Shirabesho E376294 entity
Predicate replacedBy P101 FINISHED
Object Kaiseijo
Kaiseijo was a key early Meiji-era educational institution in Japan that helped modernize the country’s higher learning and laid the groundwork for the University of Tokyo.
 E1169666 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: Kaiseijo | Statement: [Bansho Shirabesho, replacedBy, Kaiseijo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Kaiseijo
Context triple: [Bansho Shirabesho, replacedBy, Kaiseijo]
  • A. Kusasi
    Kusasi are an ethnic group primarily inhabiting northeastern Ghana and parts of Burkina Faso, known for their distinct language, farming traditions, and rich cultural festivals.
  • B. Kasagi
    Kasagi is a small town in Kyoto Prefecture, Japan, known for its scenic river landscapes and historic temples.
  • C. Kitasenju
    Kitasenju is a major commercial and transportation hub in Adachi, Tokyo, known for its busy train station, shopping complexes, and urban downtown atmosphere.
  • D. Yasakaji
    Yasakaji is a Buddhist temple in Japan known as Temple 47 on the Shikoku Pilgrimage.
  • E. Kibushi
    Kibushi is a Bantu language spoken primarily in Mayotte, where it serves as one of the island’s main regional languages.
  • 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: Kaiseijo
Triple: [Bansho Shirabesho, replacedBy, Kaiseijo]
Generated description
Kaiseijo was a key early Meiji-era educational institution in Japan that helped modernize the country’s higher learning and laid the groundwork for the University of Tokyo.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Kaiseijo
Target entity description: Kaiseijo was a key early Meiji-era educational institution in Japan that helped modernize the country’s higher learning and laid the groundwork for the University of Tokyo.
  • A. Kusasi
    Kusasi are an ethnic group primarily inhabiting northeastern Ghana and parts of Burkina Faso, known for their distinct language, farming traditions, and rich cultural festivals.
  • B. Kasagi
    Kasagi is a small town in Kyoto Prefecture, Japan, known for its scenic river landscapes and historic temples.
  • C. Kitasenju
    Kitasenju is a major commercial and transportation hub in Adachi, Tokyo, known for its busy train station, shopping complexes, and urban downtown atmosphere.
  • D. Yasakaji
    Yasakaji is a Buddhist temple in Japan known as Temple 47 on the Shikoku Pilgrimage.
  • E. Kibushi
    Kibushi is a Bantu language spoken primarily in Mayotte, where it serves as one of the island’s main regional languages.
  • 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_69d85cd1564c8190991adda63bfab4b0 completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69e04eeed2d48190a7a8a618d90012d0 completed April 16, 2026, 2:52 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff67957ebc8190b187f557bd01d58d completed May 9, 2026, 4:57 p.m.
NEDg Description generation batch_69ff68af38848190975e374b2c8c917b completed May 9, 2026, 5:02 p.m.
NED2 Entity disambiguation (via description) batch_69ff69660b6c819082dbdf06db1c8fe3 completed May 9, 2026, 5:05 p.m.
Created at: April 10, 2026, 4:15 a.m.