Triple

T8032400
Position Surface form Disambiguated ID Type / Status
Subject Villa Monastero E187018 entity
Predicate location P40 FINISHED
Object Varenna E35777 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: Varenna | Statement: [Villa Monastero, location, Varenna]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Varenna
Context triple: [Villa Monastero, location, Varenna]
  • A. Varenna chosen
    Varenna is a picturesque historic village in northern Italy known for its colorful lakeside houses, romantic promenades, and scenic views over Lake Como.
  • B. Ornavasso
    Ornavasso is a town in northern Italy notable for its rich archaeological remains linked to the ancient Lepontic Celtic culture.
  • C. Baveno
    Baveno is a picturesque lakeside town in northern Italy, known for its scenic views of Lake Maggiore and its historic villas and churches.
  • D. Possagno
    Possagno is a small town in the Veneto region of northern Italy, best known as the birthplace of the renowned Neoclassical sculptor Antonio Canova.
  • E. Lazise
    Lazise is a historic lakeside town on the eastern shore of Lake Garda in northern Italy, known for its medieval castle, picturesque harbor, and tourism.
  • 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_69ca82ae2d1081909dbfee42b41db419 completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cb3ef18da48190835454a5eb969da7 completed March 31, 2026, 3:26 a.m.
NED1 Entity disambiguation (via context triple) batch_69cd6759e83c8190869732f955279cee completed April 1, 2026, 6:43 p.m.
Created at: March 30, 2026, 5:22 p.m.