diff --git a/inference/core/workflows/execution_engine/v1/compiler/graph_constructor.py b/inference/core/workflows/execution_engine/v1/compiler/graph_constructor.py
index c95d5ca02a..7da8b4b7a7 100644
--- a/inference/core/workflows/execution_engine/v1/compiler/graph_constructor.py
+++ b/inference/core/workflows/execution_engine/v1/compiler/graph_constructor.py
@@ -735,10 +735,12 @@ def denote_data_flow_for_step(
         )
     )
     input_dimensionality_offsets = manifest.get_input_dimensionality_offsets()
+    print("input_dimensionality_offsets", input_dimensionality_offsets)
     verify_step_input_dimensionality_offsets(
         step_name=step_name,
         input_dimensionality_offsets=input_dimensionality_offsets,
     )
+    print("scalar_parameters_to_be_batched", scalar_parameters_to_be_batched)
     inputs_dimensionalities = get_inputs_dimensionalities(
         step_name=step_name,
         step_type=manifest.type,
@@ -746,14 +748,18 @@ def denote_data_flow_for_step(
         scalar_parameters_to_be_batched=scalar_parameters_to_be_batched,
         input_dimensionality_offsets=input_dimensionality_offsets,
     )
+    print("inputs_dimensionalities", inputs_dimensionalities)
     logger.debug(
         f"For step: {node}, detected the following input dimensionalities: {inputs_dimensionalities}"
     )
     parameters_with_batch_inputs = grab_parameters_defining_batch_inputs(
         inputs_dimensionalities=inputs_dimensionalities,
     )
+    print("parameters_with_batch_inputs", parameters_with_batch_inputs)
     dimensionality_reference_property = manifest.get_dimensionality_reference_property()
+    print("dimensionality_reference_property", dimensionality_reference_property)
     output_dimensionality_offset = manifest.get_output_dimensionality_offset()
+    print("output_dimensionality_offset", output_dimensionality_offset)
     verify_step_input_dimensionality_offsets(
         step_name=step_name,
         input_dimensionality_offsets=input_dimensionality_offsets,
@@ -812,6 +818,8 @@ def denote_data_flow_for_step(
         scalar_parameters_to_be_batched=scalar_parameters_to_be_batched,
     )
     step_node_data.auto_batch_casting_lineage_supports = lineage_supports
+    print("lineage_supports", lineage_supports)
+    print("Data lineage of block output", data_lineage)
     if data_lineage:
         on_top_level_lineage_denoted(data_lineage[0])
     step_node_data.data_lineage = data_lineage
@@ -1563,10 +1571,10 @@ def retrieve_batch_compatibility_of_input_selectors(
 ) -> Dict[str, Set[bool]]:
     batch_compatibility_of_properties = defaultdict(set)
     for parsed_selector in input_selectors:
+        property_name = parsed_selector.definition.property_name
+        target_set = batch_compatibility_of_properties[property_name]
         for reference in parsed_selector.definition.allowed_references:
-            batch_compatibility_of_properties[
-                parsed_selector.definition.property_name
-            ].update(reference.points_to_batch)
+            target_set |= reference.points_to_batch
     return batch_compatibility_of_properties
 
 
@@ -1606,6 +1614,9 @@ def verify_declared_batch_compatibility_against_actual_inputs(
             )
         if batch_compatibility == {True} and False in actual_input_is_batch:
             scalar_parameters_to_be_batched.add(property_name)
+        print(
+            f"property_name: {property_name}, batch_compatibility={batch_compatibility}, actual_input_is_batch={actual_input_is_batch}, step_accepts_batch_input={step_accepts_batch_input}"
+        )
     return scalar_parameters_to_be_batched
 
 
@@ -1654,6 +1665,7 @@ def get_lineage_support_for_auto_batch_casted_parameters(
             casted_dimensionality=parameter_dimensionality,
             lineage_support=lineage_support,
         )
+    print("DUMMY", result)
     return result