Triple
T2333194
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Édouard Manet |
E44249
|
entity |
| Predicate | influencedBy |
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: [Édouard Manet, influencedBy, Francisco Goya]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Francisco Goya Context triple: [Édouard Manet, influencedBy, 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.
Zurbarán
Zurbarán was a 17th-century Spanish Baroque painter renowned for his starkly realistic religious scenes and masterful use of chiaroscuro.
-
D.
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.
-
E.
Fernando Velázquez
Fernando Velázquez is a Spanish film composer and conductor known for his evocative orchestral scores for movies such as "The Orphanage," "The Impossible," and "Crimson Peak."
- 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_69a889132b488190bbb43ad4780ddd92 |
completed | March 4, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69abc66d7ea081908867ff494b70df1e |
completed | March 7, 2026, 6:32 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae8979bca08190aac46ef3dc1a2be2 |
completed | March 9, 2026, 8:48 a.m. |
Created at: March 4, 2026, 7:51 p.m.