Triple

T22335475
Position Surface form Disambiguated ID Type / Status
Subject Canavese E552134 entity
Predicate borders P224 FINISHED
Object Biellese 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: Biellese | Statement: [Canavese, borders, Biellese]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Biellese
Context triple: [Canavese, borders, Biellese]
  • A. Biellese chosen
    Biellese refers to people or things originating from Biella, a city in the Piedmont region of northern Italy known for its textile and wool industry.
  • B. Plaridel
    Plaridel is a municipality in the province of Quezon in the Philippines, known for its predominantly agricultural economy and rural communities.
  • C. Plaridel
    Plaridel is a municipality in the province of Bulacan in the Philippines, known for its historical significance and proximity to Metro Manila.
  • D. Plaridel
    Plaridel is a barangay (village-level administrative division) within the city of Sagay in the Philippines.
  • E. Mariveles
    Mariveles is a coastal municipality at the southern tip of the Bataan Peninsula in the Philippines, known for its deep-water port, industrial zones, and role in World War II history.
  • 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_69e11e494eec81909c4d2d51f69499d9 completed April 16, 2026, 5:37 p.m.
NER Named-entity recognition batch_69f1577e35f48190b11789d80182653e completed April 29, 2026, 12:57 a.m.
Created at: April 16, 2026, 8:43 p.m.