Triple
T25020683
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Giambi ed epodi |
E626560
|
entity |
| Predicate | usesClassicalModels |
P107384
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Giambi ed epodi, usesClassicalModels, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesClassicalModels Context triple: [Giambi ed epodi, usesClassicalModels, yes]
-
A.
classicalityCriterion
Indicates that a condition or set of conditions is satisfied under which a system, behavior, or description can be regarded as classical rather than quantum or non-classical.
-
B.
usesModelsType
chosen
Indicates that one entity employs or relies on a specific type or category of models in its operation or behavior.
-
C.
preservesClassicalValues
Indicates that the subject maintains, upholds, or protects traditional or classical principles, norms, or standards in the relevant context.
-
D.
usesBusinessModel
Indicates that one entity operates according to, or applies in practice, the business model defined or provided by another entity.
-
E.
isModelFree
Indicates that the behavior, decision, or control process does not rely on an internal model of the environment’s dynamics, but instead uses direct value estimates or cached experiences.
- 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_69e2ff28ee3881909c626af002457a4a |
completed | April 18, 2026, 3:48 a.m. |
| NER | Named-entity recognition | batch_69f606c79ad081908369605f72e65ca6 |
completed | May 2, 2026, 2:14 p.m. |
| PD | Predicate disambiguation | batch_69f602ce79ec8190b8336c2b9de18ac7 |
completed | May 2, 2026, 1:57 p.m. |
Created at: April 18, 2026, 6:06 a.m.