Triple
T32879114
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | workshop of Taddeo Zuccari |
E841006
|
entity |
| Predicate | trainedInStyle |
P121907
|
FINISHED |
| Object | Mannerist style |
—
|
LITERAL 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: Mannerist style | Statement: [workshop of Taddeo Zuccari, trainedInStyle, Mannerist style]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: trainedInStyle Context triple: [workshop of Taddeo Zuccari, trainedInStyle, Mannerist style]
-
A.
trainedAs
Indicates that one entity has received education or instruction to perform the role, profession, or function represented by another entity.
-
B.
inTheStyleOf
chosen
Indicates that one entity is created, performed, or presented in a manner that imitates or closely resembles the characteristic style of another entity.
-
C.
trainedAccordingTo
Indicates that an entity has been trained or instructed in alignment with a specified method, standard, guideline, or curriculum.
-
D.
dedicatedToStyleOf
Indicates that something is devoted or specifically tailored to a particular style or manner of expression.
-
E.
styleSpecialty
Indicates a relationship where an entity’s expertise, focus, or specialization is in a particular style or stylistic approach.
- F. None of above.
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_69f349436ee88190b72ee12d0f3f508e |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69f70b966860819089cf92927f47c5f1 |
completed | May 3, 2026, 8:47 a.m. |
| PD | Predicate disambiguation | batch_69f70abe43e08190b2a30930d96247c1 |
completed | May 3, 2026, 8:43 a.m. |
Created at: May 1, 2026, 1:18 a.m.