Triple

T22904417
Position Surface form Disambiguated ID Type / Status
Subject Hakone Tozan Railway E568406 entity
Predicate locatedIn P40 FINISHED
Object Hakone NE NERFINISHED

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: Hakone | Statement: [Hakone Tozan Railway, locatedIn, Hakone]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Hakone
Context triple: [Hakone Tozan Railway, locatedIn, Hakone]
  • A. Hakone chosen
    Hakone is a popular hot spring resort town in Japan known for its views of Mount Fuji, scenic lakes, and traditional ryokan inns.
  • B. Kamikochi
    Kamikochi is a scenic highland valley in Japan’s Northern Alps renowned for its pristine river, mountain views, and popular hiking trails.
  • C. Nikko
    Nikko was a principal disciple and successor of the Japanese Buddhist reformer Nichiren, known for helping to establish and spread Nichiren Buddhism.
  • D. Nikko
    Nikko is a historic Japanese city in Tochigi Prefecture renowned for its ornate UNESCO-listed shrines, temples, and scenic mountainous landscapes.
  • E. Fujiyama Onsen
    Fujiyama Onsen is a traditional Japanese hot spring facility near Mount Fuji, known for its relaxing baths and scenic views in the Fujikawaguchiko area.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e2458cd9e48190943ad2e34485d939 completed April 17, 2026, 2:37 p.m.
NER Named-entity recognition batch_69f1801895f48190bb8d49a41feac7ef completed April 29, 2026, 3:50 a.m.
Created at: April 17, 2026, 3:41 p.m.