Triple

T20134995
Position Surface form Disambiguated ID Type / Status
Subject Arpitanie E491001 entity
Predicate partOf P40 FINISHED
Object Alpine region 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: Alpine region | Statement: [Arpitanie, partOf, Alpine region]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Alpine region
Context triple: [Arpitanie, partOf, Alpine region]
  • A. Alps region chosen
    The Alps region is a major European mountainous area spanning several countries, known for its high peaks, ski resorts, and scenic landscapes.
  • B. Monte Rosa region
    The Monte Rosa region is a high-altitude Alpine area around the Monte Rosa massif, known for its extensive ski terrain, mountaineering routes, and scenic cross-border landscapes between Italy and Switzerland.
  • C. Alpine
    Alpine is a small, affluent city at the base of the Wasatch Range in northern Utah, known for its scenic mountain views and residential character.
  • D. Alpine
    Alpine is a small city in the Big Bend region of West Texas, known as a gateway to nearby desert and mountain landscapes and home to Sul Ross State University.
  • E. Alpine
    Alpine is a French sports car manufacturer renowned for its lightweight performance vehicles and historic success in rally racing.
  • 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_69da62651a0c8190a3e05e95e056a66b completed April 11, 2026, 3:01 p.m.
NER Named-entity recognition batch_69e66766e46c81908721fd47066dc9f8 completed April 20, 2026, 5:50 p.m.
Created at: April 11, 2026, 11:32 p.m.