diff --git a/nvtabular/ops/categorify.py b/nvtabular/ops/categorify.py
index 556e2a005a..ba63b72778 100644
--- a/nvtabular/ops/categorify.py
+++ b/nvtabular/ops/categorify.py
@@ -1615,6 +1615,8 @@ def _encode(
     selection_r = ColumnSelector(name if isinstance(name, list) else [storage_name])
     list_col = is_list_col(selection_l, df)
 
+    tmp_label_column = "__labels_tmp"
+
     # Find number of oov buckets
     if buckets and storage_name in buckets:
         num_oov_buckets = buckets[storage_name]
@@ -1642,9 +1644,12 @@ def _encode(
                         cats_only=True,
                         reader=read_pq_func,
                     )
-                    if len(value) and value["labels"].iloc[0] < OOV_OFFSET + num_oov_buckets:
+                    if (
+                        len(value)
+                        and value[tmp_label_column].iloc[0] < OOV_OFFSET + num_oov_buckets
+                    ):
                         # See: https://github.com/rapidsai/cudf/issues/12837
-                        value["labels"] += OOV_OFFSET + num_oov_buckets
+                        value[tmp_label_column] += OOV_OFFSET + num_oov_buckets
         else:
             value = read_pq_func(  # pylint: disable=unexpected-keyword-arg
                 path,
@@ -1652,7 +1657,7 @@ def _encode(
                 **({"split_row_groups": False} if split_out > 1 else {}),
             )
 
-            value.index = value.index.rename("labels")
+            value.index = value.index.rename(tmp_label_column)
             if split_out > 1:
                 value = value.reset_index(drop=False)
                 if type(df).__module__.split(".")[0] == "cudf":
@@ -1665,7 +1670,7 @@ def _encode(
                         part_size = file_frag.metadata.num_rows
                         ranges.append((size, size + part_size))
                         size += part_size
-                    value["labels"] = dd.from_map(lambda r: pd.RangeIndex(*r), ranges)
+                    value[tmp_label_column] = dd.from_map(lambda r: pd.RangeIndex(*r), ranges)
             else:
                 value.reset_index(drop=False, inplace=True)
 
@@ -1674,7 +1679,7 @@ def _encode(
         for c in selection_r.names:
             typ = df[selection_l.names[0]].dtype if len(selection_l.names) == 1 else df[c].dtype
             value[c] = nullable_series([None], df, typ)
-        value.index = value.index.rename("labels")
+        value.index = value.index.rename(tmp_label_column)
         value.reset_index(drop=False, inplace=True)
 
     use_collection = isinstance(value, DaskDataFrame)
@@ -1684,7 +1689,7 @@ def _encode(
         use_collection = False
 
     # Determine encoding offsets
-    null_encoding_offset = value["labels"].head(1).iloc[0] if single_table else NULL_OFFSET
+    null_encoding_offset = value[tmp_label_column].head(1).iloc[0] if single_table else NULL_OFFSET
     bucket_encoding_offset = null_encoding_offset + 1  # 2 (if not single_table)
     distinct_encoding_offset = bucket_encoding_offset + num_oov_buckets
 
@@ -1727,7 +1732,7 @@ def _encode(
                             left_on=selection_l.names,
                             right_on=selection_r.names,
                             how="left",
-                        ).dropna(subset=["labels"])
+                        ).dropna(subset=[tmp_label_column])
                         for part in value.partitions
                     ],
                     ignore_index=False,
@@ -1741,11 +1746,11 @@ def _encode(
             if len(merged_df) < len(codes):
                 # Missing nulls
                 labels = df._constructor_sliced(indistinct)
-                labels.iloc[merged_df["order"]] = merged_df["labels"]
+                labels.iloc[merged_df["order"]] = merged_df[tmp_label_column]
                 labels = labels.values
             else:
-                merged_df["labels"].fillna(df._constructor_sliced(indistinct), inplace=True)
-                labels = merged_df["labels"].values
+                merged_df[tmp_label_column].fillna(df._constructor_sliced(indistinct), inplace=True)
+                labels = merged_df[tmp_label_column].values
         else:
             # no hashing
             if use_collection:
@@ -1757,7 +1762,7 @@ def _encode(
                             left_on=selection_l.names,
                             right_on=selection_r.names,
                             how="left",
-                        ).dropna(subset=["labels"])
+                        ).dropna(subset=[tmp_label_column])
                         for part in value.partitions
                     ],
                     ignore_index=True,
@@ -1768,16 +1773,16 @@ def _encode(
                         np.full(
                             len(codes),
                             indistinct,
-                            like=merged_df["labels"].values,
+                            like=merged_df[tmp_label_column].values,
                         ),
                     )
-                    labels.iloc[merged_df["order"]] = merged_df["labels"]
+                    labels.iloc[merged_df["order"]] = merged_df[tmp_label_column]
                 else:
-                    labels = merged_df.sort_values("order")["labels"].reset_index(drop=True)
+                    labels = merged_df.sort_values("order")[tmp_label_column].reset_index(drop=True)
             else:
                 labels = codes.merge(
                     value, left_on=selection_l.names, right_on=selection_r.names, how="left"
-                ).sort_values("order")["labels"]
+                ).sort_values("order")[tmp_label_column]
             labels.fillna(indistinct, inplace=True)
             labels = labels.values
     else: