Triple

T9704433
Position Surface form Disambiguated ID Type / Status
Subject Infante Luis of Spain E234862 entity
Predicate patronOf P2320 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: [Infante Luis of Spain, patronOf, Francisco de Goya]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Francisco de Goya
Context triple: [Infante Luis of Spain, patronOf, 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_69ca84cc78808190a56f3402b7c139a7 completed March 30, 2026, 2:12 p.m.
NER Named-entity recognition batch_69cd9d74afb4819084174aab5bcdb6e0 completed April 1, 2026, 10:34 p.m.
NED1 Entity disambiguation (via context triple) batch_69d19136b40c8190922052dd84d49f15 completed April 4, 2026, 10:31 p.m.
Created at: March 30, 2026, 8:18 p.m.