diff --git a/graphstorm-processing/graphstorm_processing/constants.py b/graphstorm-processing/graphstorm_processing/constants.py
index a732306ab8..1c1ce5282f 100644
--- a/graphstorm-processing/graphstorm_processing/constants.py
+++ b/graphstorm-processing/graphstorm_processing/constants.py
@@ -31,6 +31,7 @@
 MAX_VALUE = "MAX_VALUE"
 COLUMN_NAME = "COLUMN_NAME"
 VALUE_COUNTS = "VALUE_COUNTS"
+COLUMN_ORDER_FLAG = "label_property_order_id"
 
 ############## Spark-specific constants #####################
 SPECIAL_CHARACTERS = {".", "+", "*", "?", "^", "$", "(", ")", "[", "]", "{", "}", "|", "\\"}
diff --git a/graphstorm-processing/graphstorm_processing/data_transformations/dist_transformations/dist_label_transformation.py b/graphstorm-processing/graphstorm_processing/data_transformations/dist_transformations/dist_label_transformation.py
index 840654dcd8..a69c105614 100644
--- a/graphstorm-processing/graphstorm_processing/data_transformations/dist_transformations/dist_label_transformation.py
+++ b/graphstorm-processing/graphstorm_processing/data_transformations/dist_transformations/dist_label_transformation.py
@@ -20,6 +20,7 @@
 from pyspark.sql import DataFrame, functions as F, SparkSession
 from pyspark.ml.feature import StringIndexer
 
+from graphstorm_processing.constants import COLUMN_ORDER_FLAG
 from .base_dist_transformation import DistributedTransformation
 from . import DistMultiCategoryTransformation
 from ..spark_utils import safe_rename_column
@@ -47,6 +48,7 @@ def apply(self, input_df: DataFrame) -> DataFrame:
         assert self.spark
         processed_col_name = self.label_column + "_processed"
 
+        input_df = input_df.withColumn(COLUMN_ORDER_FLAG, F.monotonically_increasing_id())
         str_indexer = StringIndexer(
             inputCol=self.label_column,
             outputCol=processed_col_name,
@@ -60,16 +62,20 @@ def apply(self, input_df: DataFrame) -> DataFrame:
             processed_col_name,
             self.label_column,
         )
+        input_df = input_df.orderBy(COLUMN_ORDER_FLAG).drop(COLUMN_ORDER_FLAG)
 
         # Labels that were missing and were assigned the value numLabels by the StringIndexer
         # are converted to None
-        long_class_label = indexed_df.select(F.col(self.label_column).cast("long")).select(
+        long_class_label = indexed_df.select(
+            F.col(self.label_column).cast("long"), F.col(COLUMN_ORDER_FLAG)
+        ).select(
             F.when(
                 F.col(self.label_column) == len(str_indexer_model.labelsArray[0]),  # type: ignore
                 F.lit(None),
             )
             .otherwise(F.col(self.label_column))
-            .alias(self.label_column)
+            .alias(self.label_column),
+            F.col(COLUMN_ORDER_FLAG),
         )
 
         # Get a mapping from original label to encoded value
@@ -85,6 +91,7 @@ def apply(self, input_df: DataFrame) -> DataFrame:
             map_dict = json.loads(mapping_str)
             self.value_map[map_dict[self.label_column]] = map_dict[processed_col_name]
 
+        long_class_label = long_class_label.orderBy(COLUMN_ORDER_FLAG)
         return long_class_label
 
     @staticmethod
diff --git a/graphstorm-processing/tests/test_dist_label_loader.py b/graphstorm-processing/tests/test_dist_label_loader.py
index ce5e6b0fe7..4713474ecd 100644
--- a/graphstorm-processing/tests/test_dist_label_loader.py
+++ b/graphstorm-processing/tests/test_dist_label_loader.py
@@ -16,7 +16,7 @@
 
 import numpy as np
 
-from pyspark.sql import DataFrame, SparkSession
+from pyspark.sql import DataFrame, SparkSession, Row
 from pyspark.sql.types import StructField, StructType, StringType
 
 
@@ -133,3 +133,34 @@ def test_dist_multilabel_classification(spark: SparkSession, check_df_schema):
             assert row_val[2 * i + 1] == 1.0
         else:
             assert i == 4, "Only the last row should be None/null"
+
+
+def test_dist_label_order(spark: SparkSession, check_df_schema):
+    label_col = "name"
+    classification_config = {
+        "column": "name",
+        "type": "classification",
+        "split_rate": {"train": 0.8, "val": 0.2, "test": 0.0},
+    }
+
+    data_zeros = [Row(value=0) for _ in range(5000)]
+    data_ones = [Row(value=1) for _ in range(5000)]
+    data = data_zeros + data_ones
+    names_df = spark.createDataFrame(data, schema=[label_col])
+
+    label_transformer = DistLabelLoader(LabelConfig(classification_config), spark)
+
+    transformed_labels = label_transformer.process_label(names_df)
+    label_map = label_transformer.label_map
+
+    assert set(label_map.keys()) == {"0", "1"}
+
+    check_df_schema(transformed_labels)
+
+    first_5000 = transformed_labels.limit(5000).collect()
+    first_5000_check = all(row.name == 0 for row in first_5000)
+
+    next_5000 = transformed_labels.limit(10000).subtract(transformed_labels.limit(5000)).collect()
+    next_5000_check = all(row.name == 1 for row in next_5000)
+
+    assert first_5000_check and next_5000_check, "The value assignment is in disorder"