Triple

T15198976
Position Surface form Disambiguated ID Type / Status
Subject Prince of Girona E363213 entity
Predicate namedAfter P63 FINISHED
Object City of Girona E80146 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: City of Girona | Statement: [Prince of Girona, namedAfter, City of Girona]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: City of Girona
Context triple: [Prince of Girona, namedAfter, City of Girona]
  • A. Girona chosen
    Girona is a historic city in northeastern Catalonia, Spain, known for its well-preserved medieval architecture, walled Old Quarter, and prominent cathedral.
  • B. Girona Barri Vell
    Girona Barri Vell is the historic old town of Girona, Spain, known for its medieval architecture, narrow cobbled streets, and well-preserved landmarks.
  • C. Martorell
    Martorell is a town in Catalonia, Spain, known as an important industrial hub within the Barcelona metropolitan area.
  • D. Begur
    Begur is a picturesque coastal town in Catalonia, Spain, known for its medieval hilltop castle, charming old quarter, and scenic beaches along the Costa Brava.
  • E. Lleida old quarter
    Lleida old quarter is the historic center of Lleida, Spain, characterized by its medieval streets, heritage buildings, and cultural landmarks.
  • 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_69d85a0b78bc8190b6e5ad51a2c4cfc5 completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e006b476208190a5119710c518bb1f completed April 15, 2026, 9:44 p.m.
NED1 Entity disambiguation (via context triple) batch_6a00c28d1e1c81909e869f01659ec233 completed May 10, 2026, 5:38 p.m.
Created at: April 10, 2026, 3:10 a.m.