Triple

T20634737
Position Surface form Disambiguated ID Type / Status
Subject Renault Alaskan E507047 entity
Predicate assemblyLocation P40 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: [Renault Alaskan, assemblyLocation, Barcelona, Spain]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Barcelona, Spain
Context triple: [Renault Alaskan, assemblyLocation, 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_69e0b4bd4a0081908d4e97a590a33fb2 completed April 16, 2026, 10:06 a.m.
NER Named-entity recognition batch_69e6ad0e5fc481909e4f0dd7fb1203fc completed April 20, 2026, 10:47 p.m.
Created at: April 16, 2026, 11:42 a.m.