Triple

T17999838
Position Surface form Disambiguated ID Type / Status
Subject Sandra E430597 entity
Predicate filmingLocation P40 FINISHED
Object Volterra, Italy 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: Volterra, Italy | Statement: [Sandra, filmingLocation, Volterra, Italy]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Volterra, Italy
Context triple: [Sandra, filmingLocation, Volterra, Italy]
  • A. Volterra chosen
    Volterra is an ancient hilltop town in Tuscany, Italy, renowned for its Etruscan origins, medieval architecture, and traditional alabaster craftsmanship.
  • B. Vaiano, Italy
    Vaiano, Italy is a small Tuscan town in the Province of Prato, known for its scenic setting in the Bisenzio Valley and as the birthplace of famed cyclist Fiorenzo Magni.
  • C. Varano de' Melegari, Italy
    Varano de' Melegari is a small town in Italy’s Emilia-Romagna region known for its strong motorsport heritage and as a hub of automotive engineering.
  • D. Vinadio, Italy
    Vinadio, Italy is a small alpine municipality in the Piedmont region near the French border, known for its historic fortifications and mountain landscapes.
  • E. Montella, Italy
    Montella, Italy is a small town in the Campania region of southern Italy, known for its mountainous landscape and traditional chestnut production.
  • 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_69d8b90364248190a37381adea932f42 completed April 10, 2026, 8:46 a.m.
NER Named-entity recognition batch_69e4b3e75e908190a6ff6a3ec6069ff5 completed April 19, 2026, 10:52 a.m.
Created at: April 10, 2026, 10:23 a.m.