Triple

T8354607
Position Surface form Disambiguated ID Type / Status
Subject Mount Ontake E196652 entity
Predicate nearbyCity P350 FINISHED
Object Kiso
Kiso is a town in Nagano Prefecture, Japan, known for its scenic Kiso Valley, traditional post towns on the old Nakasendō route, and proximity to Mount Ontake.
 E727568 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: Kiso | Statement: [Mount Ontake, nearbyCity, Kiso]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Kiso
Context triple: [Mount Ontake, nearbyCity, Kiso]
  • A. Kipoi
    Kipoi is a traditional stone-built village in the Zagori region of Epirus, northwestern Greece, known for its arched bridges and well-preserved architecture.
  • B. Kizu
    Kizu was a former town in Japan’s Kyoto Prefecture that was once part of Soraku District before being merged into a larger municipality.
  • C. Kisukuma
    Kisukuma is a Bantu language spoken primarily by the Sukuma people in northern Tanzania.
  • D. Kasagi
    Kasagi is a small town in Kyoto Prefecture, Japan, known for its scenic river landscapes and historic temples.
  • E. Ōiso
    Ōiso is a coastal town in Kanagawa Prefecture, Japan, known as a historic seaside resort and former political retreat.
  • 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: Kiso
Triple: [Mount Ontake, nearbyCity, Kiso]
Generated description
Kiso is a town in Nagano Prefecture, Japan, known for its scenic Kiso Valley, traditional post towns on the old Nakasendō route, and proximity to Mount Ontake.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Kiso
Target entity description: Kiso is a town in Nagano Prefecture, Japan, known for its scenic Kiso Valley, traditional post towns on the old Nakasendō route, and proximity to Mount Ontake.
  • A. Kipoi
    Kipoi is a traditional stone-built village in the Zagori region of Epirus, northwestern Greece, known for its arched bridges and well-preserved architecture.
  • B. Kizu
    Kizu was a former town in Japan’s Kyoto Prefecture that was once part of Soraku District before being merged into a larger municipality.
  • C. Kisukuma
    Kisukuma is a Bantu language spoken primarily by the Sukuma people in northern Tanzania.
  • D. Kasagi
    Kasagi is a small town in Kyoto Prefecture, Japan, known for its scenic river landscapes and historic temples.
  • E. Ōiso
    Ōiso is a coastal town in Kanagawa Prefecture, Japan, known as a historic seaside resort and former political retreat.
  • 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_69ca82f08b348190bfb7881944bbff6f completed March 30, 2026, 2:04 p.m.
NER Named-entity recognition batch_69cb8048edb88190a1980ad74818b898 completed March 31, 2026, 8:05 a.m.
NED1 Entity disambiguation (via context triple) batch_69cdc75e94288190ba1905dd4ca172dd completed April 2, 2026, 1:33 a.m.
NEDg Description generation batch_69cdcc86626c8190a4206feedea24b41 completed April 2, 2026, 1:55 a.m.
NED2 Entity disambiguation (via description) batch_69cdcde02e088190be8220f7d18d6700 completed April 2, 2026, 2:01 a.m.
Created at: March 30, 2026, 5:59 p.m.