Triple

T9462833
Position Surface form Disambiguated ID Type / Status
Subject José de Garnica E228192 entity
Predicate familyName P18 FINISHED
Object de Garnica E228192 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: de Garnica | Statement: [José de Garnica, familyName, de Garnica]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: de Garnica
Context triple: [José de Garnica, familyName, de Garnica]
  • A. de Garnica chosen
    De Garnica is a Spanish surname associated with individuals such as José de Garnica.
  • B. Albuhera
    Albuhera is a village in southwestern Spain that was the site of a major 1811 Peninsular War battle between British-led allied forces and the French.
  • C. Gernika-Lumo
    Gernika-Lumo is a historic town in the Basque Country of northern Spain, internationally known for the 1937 bombing that inspired Pablo Picasso’s famous painting "Guernica."
  • D. Jarama
    The Jarama is a major river in central Spain that flows through the Community of Madrid and Castile-La Mancha before joining the Tagus River.
  • E. Garraf
    Garraf is a coastal comarca in Catalonia, Spain, known for its Mediterranean landscapes, natural park, and seaside towns such as Sitges and Vilanova i la Geltrú.
  • 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_69ca846fee388190a6ec273fd644b88b completed March 30, 2026, 2:10 p.m.
NER Named-entity recognition batch_69cd7fcd9794819093c392489d4efbe9 completed April 1, 2026, 8:27 p.m.
NED1 Entity disambiguation (via context triple) batch_69d139ece5bc81908956e1f7ecbb1aa4 completed April 4, 2026, 4:18 p.m.
Created at: March 30, 2026, 7:53 p.m.