Triple
T16145411
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | William Hogarth |
E391766
|
entity |
| Predicate | influenced |
P9
|
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: [William Hogarth, influenced, Francisco Goya]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Francisco Goya Context triple: [William Hogarth, influenced, 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 Toledo
Goya Toledo is a Spanish actress and former model best known internationally for her role in the acclaimed film "Amores perros."
-
C.
Goya
Goya is Habana Labs’ AI inference processor designed to accelerate deep learning workloads with high efficiency and scalability.
-
D.
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.
-
E.
Valencia Joaquín Sorolla
Valencia Joaquín Sorolla is a major high-speed railway station in Valencia, Spain, serving as a key hub for AVE and long-distance train services.
- 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_69d87f1c65e48190aa2b4c472e9bafc4 |
completed | April 10, 2026, 4:39 a.m. |
| NER | Named-entity recognition | batch_69e21d9376fc8190bd9ef586b00c1d3b |
completed | April 17, 2026, 11:46 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fff2b9322c8190a773681679f9ad79 |
completed | May 10, 2026, 2:51 a.m. |
Created at: April 10, 2026, 5:01 a.m.