# Neptune ML predicates used in SPARQL inference queries
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The following predicates are used with SPARQL inference:

## `neptune-ml:timeout` predicate
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Specifies the timeout for connection with the remote server. Should not be confused with the query request timeout, which is the maximum amount of time the server can take to satisfy a request.

Note that if the query timeout occurs before the service timeout specified by the `neptune-ml:timeout` predicate occurs, the service connection is canceled too.

## `neptune-ml:outputClass` predicate
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The `neptune-ml:outputClass` predicate is only used to define the class of the predicted object for object prediction or predicted subject for subect prediction.

## `neptune-ml:outputScore` predicate
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The `neptune-ml:outputScore` predicate is a positive number that represents the likelihood that the output of a machine learning model is correct.

## `neptune-ml:modelType` predicate
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The `neptune-ml:modelType` predicate specifies the type of machine learning model being trained:
+ `OBJECT_CLASSIFICATION`
+ `OBJECT_REGRESSION`
+ `OBJECT_PREDICTION`
+ `SUBJECT_PREDICTION`

## `neptune-ml:input` predicate
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The `neptune-ml:input` predicate refers to the list of URIs used as inputs for Neptune ML.

## `neptune-ml:output` predicate
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The `neptune-ml:output` predicate refers to the list of binding sets where Neptune ML returns results.

## `neptune-ml:predicate` predicate
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The `neptune-ml:predicate` predicate is used differently depending on the task being performed:
+ For **object or subject prediction**: defines the type of predicate (the edge or relationship type).
+ For **object classification and regression**: defines the literal (property) we want to predict.

## `neptune-ml:batchSize` predicate
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The `neptune-ml:batchSize` specifies the input size for the remote service call.