Triple

T6494221
Position Surface form Disambiguated ID Type / Status
Subject Liria Palace E148114 entity
Predicate containsWorkBy P2011 FINISHED
Object Francisco de Goya E8545 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: Francisco de Goya | Statement: [Liria Palace, containsWorkBy, Francisco de Goya]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Francisco de Goya
Context triple: [Liria Palace, containsWorkBy, Francisco de Goya]
  • A. Francisco Goya chosen
    Francisco Goya was a pioneering Spanish Romantic painter and printmaker renowned for his powerful portraits, dark and haunting imagery, and critical depictions of war and society.
  • B. Goya
    Goya is Habana Labs’ AI inference processor designed to accelerate deep learning workloads with high efficiency and scalability.
  • C. Antonio de Gaztañeta
    Antonio de Gaztañeta was a prominent early 18th-century Spanish admiral and naval architect known for modernizing the Spanish fleet and leading it in major engagements.
  • D. Zurbarán
    Zurbarán was a 17th-century Spanish Baroque painter renowned for his starkly realistic religious scenes and masterful use of chiaroscuro.
  • E. Jusepe de Ribera
    Jusepe de Ribera was a 17th-century Spanish Tenebrist painter and printmaker, renowned for his dramatic use of light and shadow and intense religious and mythological scenes.
  • 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_69c009088f3081909cd467b05919de30 completed March 22, 2026, 3:21 p.m.
NER Named-entity recognition batch_69c06ab7c0b8819091437a293b40dfd2 completed March 22, 2026, 10:18 p.m.
NED1 Entity disambiguation (via context triple) batch_69c65fdc835081909772f3a3aaee538f completed March 27, 2026, 10:45 a.m.
Created at: March 22, 2026, 4:53 p.m.