Triple

T20870377
Position Surface form Disambiguated ID Type / Status
Subject Don Matteo E513872 entity
Predicate setting P1957 FINISHED
Object Gubbio 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: Gubbio | Statement: [Don Matteo, setting, Gubbio]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Gubbio
Context triple: [Don Matteo, setting, Gubbio]
  • A. Gubbio chosen
    Gubbio is a historic medieval town in the Umbria region of central Italy, known for its well-preserved stone architecture and traditional festivals.
  • B. Città della Pieve
    Città della Pieve is a historic hilltop town in Umbria, central Italy, known for its medieval architecture and artworks by the Renaissance painter Perugino.
  • C. Perugia
    Perugia is a historic hilltop city in central Italy, renowned for its Etruscan heritage, medieval architecture, and vibrant cultural and university life.
  • D. Città di Castello
    Città di Castello is a historic town in the Umbria region of central Italy, known for its medieval architecture, Renaissance art, and location along the upper Tiber River.
  • E. Montevarchi
    Montevarchi is a Tuscan town in central Italy known for its historic center, proximity to the Arno River, and role as a commercial and cultural hub in the Valdarno 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_69e0b4f675cc8190b4e745225b62eb66 completed April 16, 2026, 10:07 a.m.
NER Named-entity recognition batch_69e6c4637ec48190830023d20fb8124c completed April 21, 2026, 12:27 a.m.
Created at: April 16, 2026, 12:45 p.m.