Triple

T1362202
Position Surface form Disambiguated ID Type / Status
Subject Inamori Foundation E29120 entity
Predicate headquartersLocation P62 FINISHED
Object Kyoto, Japan E10010 NE FINISHED

How this triple was built (2 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: Kyoto, Japan | Statement: [Inamori Foundation, headquartersLocation, Kyoto, Japan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kyoto, Japan
Context triple: [Inamori Foundation, headquartersLocation, Kyoto, Japan]
  • A. Kyoto chosen
    Kyoto is a historic Japanese city renowned for its well-preserved temples, traditional wooden houses, and role as the former imperial capital.
  • B. Kamigyo-ku, Kyoto
    Kamigyo-ku, Kyoto is a central ward of Kyoto City known for its historic neighborhoods, traditional culture, and many important temples and shrines.
  • C. Fukuroi, Japan
    Fukuroi, Japan is a city in Shizuoka Prefecture known for its agricultural production, traditional temples, and role as a regional industrial and cultural center.
  • D. Shizuoka City, Japan
    Shizuoka City, Japan is a coastal city in central Honshu known for its views of Mount Fuji, green tea production, and role as a regional economic and cultural center.
  • E. Kakegawa, Japan
    Kakegawa, Japan is a city in Shizuoka Prefecture known for its historic castle, green tea production, and scenic views of Mount Fuji.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69a498d77abc8190913bf57e5f51d2c4 completed March 1, 2026, 7:51 p.m.
NER Named-entity recognition batch_69a4c2b4ab3c8190ad692e32eee05976 completed March 1, 2026, 10:50 p.m.
NED1 Entity disambiguation (via context triple) batch_69ae719488288190b3f954c037af3d3f completed March 9, 2026, 7:07 a.m.
Created at: March 1, 2026, 7:57 p.m.