Fix footnote syntax

Author: recrwplay

doc/modules/ROOT/pages/pipelines.adoc

@@ -42,11 +42,11 @@ Below is a description of the methods on such objects:
                             Union[str, list[str]]       | Series                  | https://neo4j.com/docs/graph-data-science/current/machine-learning/node-property-prediction/nodeclassification-pipelines/config/#nodeclassification-pipelines-adding-features[Select node properties to be used as features].
 | configureSplit        | config: **kwargs              | Series                  | https://neo4j.com/docs/graph-data-science/current/machine-learning/node-property-prediction/nodeclassification-pipelines/config/#nodeclassification-pipelines-configure-splits[Configure the train-test dataset split].
 | addLogisticRegression | parameter_space: +
-                            dict[str, any]              | Series                  | https://neo4j.com/docs/graph-data-science/current/machine-learning/node-property-prediction/nodeclassification-pipelines/config/#nodeclassification-pipelines-adding-model-candidates[Add a logistic regression model configuration to train as a candidate in the model selection phase]. footnote:range[Ranges can also be given as length two `Tuple`s. I.e. `(x, y)` is the same as `{range: [x, y]}`.]
+                            dict[str, any]              | Series                  | https://neo4j.com/docs/graph-data-science/current/machine-learning/node-property-prediction/nodeclassification-pipelines/config/#nodeclassification-pipelines-adding-model-candidates[Add a logistic regression model configuration to train as a candidate in the model selection phase]. footnote:classification-range[Ranges can also be given as length two ``Tuple``s. I.e. `(x, y)` is the same as `{range: [x, y\]}`.]
 | addRandomForest       | parameter_space: +
-                            dict[str, any]              | Series                  | https://neo4j.com/docs/graph-data-science/current/machine-learning/node-property-prediction/nodeclassification-pipelines/config/#nodeclassification-pipelines-adding-model-candidates[Add a random forest model configuration to train as a candidate in the model selection phase]. footnote:range[]
+                            dict[str, any]              | Series                  | https://neo4j.com/docs/graph-data-science/current/machine-learning/node-property-prediction/nodeclassification-pipelines/config/#nodeclassification-pipelines-adding-model-candidates[Add a random forest model configuration to train as a candidate in the model selection phase]. footnote:classification-range[]
 | addMLP                | parameter_space: +
-                            dict[str, any]              | Series                  | https://neo4j.com/docs/graph-data-science/current/machine-learning/node-property-prediction/nodeclassification-pipelines/config/#nodeclassification-pipelines-adding-model-candidates[Add an MLP model configuration to train as a candidate in the model selection phase]. footnote:range[]
+                            dict[str, any]              | Series                  | https://neo4j.com/docs/graph-data-science/current/machine-learning/node-property-prediction/nodeclassification-pipelines/config/#nodeclassification-pipelines-adding-model-candidates[Add an MLP model configuration to train as a candidate in the model selection phase]. footnote:classification-range[]
 | configureAutoTuning   | config: **kwargs
                                                         | Series                  | https://neo4j.com/docs/graph-data-science/current/machine-learning/node-property-prediction/nodeclassification-pipelines/config/#nodeclassification-pipelines-configure-auto-tuning[Configure the auto-tuning].
 | train                 | G: Graph, +
@@ -221,11 +221,11 @@ Below is a description of the methods on such objects:
                         config: **kwargs                | Series              | https://neo4j.com/docs/graph-data-science/current/machine-learning/linkprediction-pipelines/config/#linkprediction-adding-features[Add a link feature for model training based on node properties and a feature combiner].
 | configureSplit        | config: **kwargs              | Series              | https://neo4j.com/docs/graph-data-science/current/machine-learning/linkprediction-pipelines/config/#linkprediction-configure-splits[Configure the feature-train-test dataset split].
 | addLogisticRegression | parameter_space: +
-                          dict[str, any]                | Series              | https://neo4j.com/docs/graph-data-science/current/machine-learning/linkprediction-pipelines/config/#linkprediction-adding-model-candidates[Add a logistic regression model configuration to train as a candidate in the model selection phase]. footnote:range[Ranges can also be given as length two `Tuple`s. I.e. `(x, y)` is the same as `{range: [x, y]}`.]
+                          dict[str, any]                | Series              | https://neo4j.com/docs/graph-data-science/current/machine-learning/linkprediction-pipelines/config/#linkprediction-adding-model-candidates[Add a logistic regression model configuration to train as a candidate in the model selection phase]. footnote:prediction-range[Ranges can also be given as length two ``Tuple``s. I.e. `(x, y)` is the same as `{range: [x, y\]}`.]
 | addRandomForest       | parameter_space: +
-                          dict[str, any]                | Series              | https://neo4j.com/docs/graph-data-science/current/machine-learning/linkprediction-pipelines/config/#linkprediction-adding-model-candidates[Add a random forest model configuration to train as a candidate in the model selection phase]. footnote:range[]
+                          dict[str, any]                | Series              | https://neo4j.com/docs/graph-data-science/current/machine-learning/linkprediction-pipelines/config/#linkprediction-adding-model-candidates[Add a random forest model configuration to train as a candidate in the model selection phase]. footnote:prediction-range[]
 | addMLP                | parameter_space: +
-                          dict[str, any]              | Series                  | https://neo4j.com/docs/graph-data-science/current/machine-learning/linkprediction-pipelines/config/#linkprediction-adding-model-candidates[Add an MLP model configuration to train as a candidate in the model selection phase]. footnote:range[]
+                          dict[str, any]              | Series                  | https://neo4j.com/docs/graph-data-science/current/machine-learning/linkprediction-pipelines/config/#linkprediction-adding-model-candidates[Add an MLP model configuration to train as a candidate in the model selection phase]. footnote:prediction-range[]
 | configureAutoTuning   | config: **kwargs
                                                         | Series              | https://neo4j.com/docs/graph-data-science/current/machine-learning/linkprediction-pipelines/config/#linkprediction-configure-auto-tuning[Configure the auto-tuning].
 | train                 | G: Graph, +
@@ -395,9 +395,9 @@ config: **kwargs        | Series                        | https://neo4j.com/docs
 Union[str, list[str]]   | Series                        | https://neo4j.com/docs/graph-data-science/current/machine-learning/node-property-prediction/noderegression-pipelines/config/#noderegression-pipelines-adding-features[Select node properties to be used as features].
 | configureSplit        | config: **kwargs              | Series                  | https://neo4j.com/docs/graph-data-science/current/machine-learning/node-property-prediction/noderegression-pipelines/config/#noderegression-pipelines-configure-splits[Configure the train-test dataset split].
 | addLinearRegression   | parameter_space: +
-dict[str, any]          | Series                        | https://neo4j.com/docs/graph-data-science/current/machine-learning/node-property-prediction/noderegression-pipelines/config/#noderegression-pipelines-adding-model-candidates[Add a linear regression model configuration to train as a candidate in the model selection phase]. footnote:range[Ranges can also be given as length two `Tuple`s. I.e. `(x, y)` is the same as `{range: [x, y]}`.]
+dict[str, any]          | Series                        | https://neo4j.com/docs/graph-data-science/current/machine-learning/node-property-prediction/noderegression-pipelines/config/#noderegression-pipelines-adding-model-candidates[Add a linear regression model configuration to train as a candidate in the model selection phase]. footnote:regression-range[Ranges can also be given as length two ``Tuple``s. I.e. `(x, y)` is the same as `{range: [x, y\]}`.]
 | addRandomForest       | parameter_space: +
-dict[str, any]          | Series                        | https://neo4j.com/docs/graph-data-science/current/machine-learning/node-property-prediction/noderegression-pipelines/config/#noderegression-pipelines-adding-model-candidates[Add a random forest model configuration to train as a candidate in the model selection phase]. footnote:range[]
+dict[str, any]          | Series                        | https://neo4j.com/docs/graph-data-science/current/machine-learning/node-property-prediction/noderegression-pipelines/config/#noderegression-pipelines-adding-model-candidates[Add a random forest model configuration to train as a candidate in the model selection phase]. footnote:regression-range[]
 | configureAutoTuning   | config: **kwargs              | Series                  | https://neo4j.com/docs/graph-data-science/current/machine-learning/node-property-prediction/noderegression-pipelines/config/#noderegression-pipelines-configure-auto-tuning[Configure the auto-tuning].
 | train                 | G: Graph, +
 config: **kwargs        | NCPredictionPipeline, +