Triple
T36489703
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bayesian optimization |
E899020
|
entity |
| Predicate | commonSurrogateModel |
P41880
|
FINISHED |
| Object | Gaussian process regression |
—
|
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: Gaussian process regression | Statement: [Bayesian optimization, commonSurrogateModel, Gaussian process regression]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: commonSurrogateModel Context triple: [Bayesian optimization, commonSurrogateModel, Gaussian process regression]
-
A.
possibleModel
chosen
Indicates that one entity can serve as a potential or candidate model or template for another entity.
-
B.
concurrentModel
Indicates that two or more processes, activities, or states occur or are valid at the same time, potentially interacting or overlapping in execution.
-
C.
associatedModelGeneration
Indicates that one entity is responsible for creating, producing, or generating another related model or representation.
-
D.
testedModel
Indicates that an entity has evaluated or examined a particular model to assess its performance, behavior, or validity.
-
E.
model
Indicates that one entity serves as a representation, example, or simulation of another entity or concept.
- 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_69f76e5ad4588190bdbce60c52fbb785 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69ff0e9c75208190a4423261f00b79b3 |
completed | May 9, 2026, 10:38 a.m. |
| PD | Predicate disambiguation | batch_69ff0e07f08481909c4ae322632a6bf0 |
completed | May 9, 2026, 10:35 a.m. |
Created at: May 3, 2026, 4:10 p.m.