Triple

T11968912
Position Surface form Disambiguated ID Type / Status
Subject No. 3 – Que viene el coco E284863 entity
Predicate hasCreator P806 FINISHED
Object Francisco 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 Goya | Statement: [No. 3 – Que viene el coco, hasCreator, Francisco Goya]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Francisco Goya
Context triple: [No. 3 – Que viene el coco, hasCreator, Francisco 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. Julio Romero de Torres
    Julio Romero de Torres was a Spanish painter renowned for his symbolist and sensual depictions of Andalusian women and popular culture in the early 20th century.
  • 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_69d6ab2eaeb881909f7914758f859413 completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d9037bee54819085242a3ef3e286f9 completed April 10, 2026, 2:04 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6c0d2818c8190a1bea5f1fc8a9f59 completed May 3, 2026, 3:28 a.m.
Created at: April 8, 2026, 9:46 p.m.