Triple

T19126383
Position Surface form Disambiguated ID Type / Status
Subject Vittorio Gregotti E468191 entity
Predicate workLocation P7 FINISHED
Object Barcelona, Spain 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: Barcelona, Spain | Statement: [Vittorio Gregotti, workLocation, Barcelona, Spain]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Barcelona, Spain
Context triple: [Vittorio Gregotti, workLocation, Barcelona, Spain]
  • A. Barcelona chosen
    Barcelona is a major Spanish Mediterranean city renowned for its distinctive Catalan culture, Gaudí architecture, and vibrant arts and nightlife scenes.
  • B. Barcelona
    Barcelona is a coastal municipality in the province of Sorsogon in the Bicol Region of the Philippines, known for its historic church and scenic seaside views.
  • C. El Masnou, Spain
    El Masnou, Spain is a coastal town in the province of Barcelona, Catalonia, known for its Mediterranean beaches and marina.
  • D. Martorell, Spain
    Martorell, Spain is a town in Catalonia best known as a major automotive manufacturing hub and home to SEAT’s main production plant.
  • E. Barcelonès
    Barcelonès is a highly urbanized comarca in Catalonia that includes the city of Barcelona and serves as one of the most densely populated areas in Spain.
  • 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_69d8dd0796a48190b34ce4cd9d3f3be5 completed April 10, 2026, 11:20 a.m.
NER Named-entity recognition batch_69e5e3cd851081909d5665b362ae08de completed April 20, 2026, 8:29 a.m.
Created at: April 10, 2026, 12:05 p.m.