Triple
T23068032
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dukes of Alba |
E575105
|
entity |
| Predicate | associatedArtist |
P2152
|
FINISHED |
| Object | Francisco Goya |
—
|
NE NERFINISHED |
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: [Dukes of Alba, associatedArtist, Francisco Goya]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Francisco Goya Context triple: [Dukes of Alba, associatedArtist, 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.
Goya
Goya is a central, upscale neighborhood in Madrid, Spain, known for its shopping streets, cultural venues, and sports arenas.
-
E.
Goya
Goya is a city in northeastern Argentina known for its agricultural production, especially tobacco, and its annual National Surubí Fishing Festival.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e245bd6e4c8190bb8942245b68cad5 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f189a5aa4081909b3f0dc92877323d |
completed | April 29, 2026, 4:31 a.m. |
Created at: April 17, 2026, 3:55 p.m.