Triple

T9865133
Position Surface form Disambiguated ID Type / Status
Subject Jon Sobrino E239812 entity
Predicate placeOfBirth P1 FINISHED
Object Barcelona, Spain E9407 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: Barcelona, Spain | Statement: [Jon Sobrino, placeOfBirth, Barcelona, Spain]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Barcelona, Spain
Context triple: [Jon Sobrino, placeOfBirth, 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. El Masnou, Spain
    El Masnou, Spain is a coastal town in the province of Barcelona, Catalonia, known for its Mediterranean beaches and marina.
  • C. 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.
  • D. 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.
  • E. Malaga, Spain
    Malaga, Spain is a historic port city on Spain’s southern Costa del Sol, known for its Mediterranean beaches, rich cultural heritage, and as the birthplace of Pablo Picasso.
  • 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_69ca84e7506c819095cbde4ff16512bb completed March 30, 2026, 2:12 p.m.
NER Named-entity recognition batch_69cdb3ba7f288190a15ebec2cc3112c4 completed April 2, 2026, 12:09 a.m.
NED1 Entity disambiguation (via context triple) batch_69d1eadd95ac81909a265db37b648df0 completed April 5, 2026, 4:53 a.m.
Created at: March 30, 2026, 8:36 p.m.