Triple

T14446750
Position Surface form Disambiguated ID Type / Status
Subject 100 Famous Japanese Mountains E358227 entity
Predicate hasPart P35 FINISHED
Object Mount Kaikoma E868901 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: Mount Kaikoma | Statement: [100 Famous Japanese Mountains, hasPart, Mount Kaikoma]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mount Kaikoma
Context triple: [100 Famous Japanese Mountains, hasPart, Mount Kaikoma]
  • A. Mount Kaikoma chosen
    Mount Kaikoma is a prominent, granite-peaked mountain in Japan’s Southern Alps, known for its rugged terrain and popular hiking and climbing routes.
  • B. Mount Kaweah
    Mount Kaweah is a prominent high peak in California’s Sierra Nevada, known for its rugged terrain and scenic alpine surroundings within Sequoia National Park.
  • C. Mount Kawi
    Mount Kawi is a stratovolcano in East Java, Indonesia, known for its scenic highland landscapes and cultural significance to local communities.
  • D. Mount Kanobili
    Mount Kanobili is a mountain in southern Georgia notable for hosting the Abastumani Astrophysical Observatory.
  • E. Mount Noro
    Mount Noro is a scenic mountain in Kure, Japan, known for its panoramic views of the Seto Inland Sea and popular hiking and sightseeing opportunities.
  • 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_69d82794dfa081909b9134ad2e32244b completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69de9160126c8190a2862a1a3dde1aff completed April 14, 2026, 7:11 p.m.
NED1 Entity disambiguation (via context triple) batch_69fed31670a48190a606e812a2aa0a6e completed May 9, 2026, 6:24 a.m.
Created at: April 10, 2026, 1:19 a.m.