diff --git a/examples/tpch/q01_pricing_summary_report.py b/examples/tpch/q01_pricing_summary_report.py
index 7e86055d..cb9485a7 100644
--- a/examples/tpch/q01_pricing_summary_report.py
+++ b/examples/tpch/q01_pricing_summary_report.py
@@ -48,9 +48,7 @@
 # want to report results for. It should be between 60-120 days before the end.
 DAYS_BEFORE_FINAL = 90
 
-# Note: this is a hack on setting the values. It should be set differently once
-# https://github.com/apache/datafusion-python/issues/665 is resolved.
-interval = pa.scalar((0, 0, DAYS_BEFORE_FINAL), type=pa.month_day_nano_interval())
+interval = pa.scalar((0, DAYS_BEFORE_FINAL, 0), type=pa.month_day_nano_interval())
 
 print("Final date in database:", greatest_ship_date)
 
diff --git a/examples/tpch/q04_order_priority_checking.py b/examples/tpch/q04_order_priority_checking.py
index 40eab697..9dbd8167 100644
--- a/examples/tpch/q04_order_priority_checking.py
+++ b/examples/tpch/q04_order_priority_checking.py
@@ -49,9 +49,7 @@
 # Create a date object from the string
 date = datetime.strptime(DATE_OF_INTEREST, "%Y-%m-%d").date()
 
-# Note: this is a hack on setting the values. It should be set differently once
-# https://github.com/apache/datafusion-python/issues/665 is resolved.
-interval = pa.scalar((0, 0, INTERVAL_DAYS), type=pa.month_day_nano_interval())
+interval = pa.scalar((0, INTERVAL_DAYS, 0), type=pa.month_day_nano_interval())
 
 # Limit results to cases where commitment date before receipt date
 # Aggregate the results so we only get one row to join with the order table.
diff --git a/examples/tpch/q05_local_supplier_volume.py b/examples/tpch/q05_local_supplier_volume.py
index 27b4b84c..f17f600a 100644
--- a/examples/tpch/q05_local_supplier_volume.py
+++ b/examples/tpch/q05_local_supplier_volume.py
@@ -41,9 +41,7 @@
 
 date = datetime.strptime(DATE_OF_INTEREST, "%Y-%m-%d").date()
 
-# Note: this is a hack on setting the values. It should be set differently once
-# https://github.com/apache/datafusion-python/issues/665 is resolved.
-interval = pa.scalar((0, 0, INTERVAL_DAYS), type=pa.month_day_nano_interval())
+interval = pa.scalar((0, INTERVAL_DAYS, 0), type=pa.month_day_nano_interval())
 
 # Load the dataframes we need
 
diff --git a/examples/tpch/q06_forecasting_revenue_change.py b/examples/tpch/q06_forecasting_revenue_change.py
index 3f58c5ec..ec98aaf5 100644
--- a/examples/tpch/q06_forecasting_revenue_change.py
+++ b/examples/tpch/q06_forecasting_revenue_change.py
@@ -45,9 +45,7 @@
 
 date = datetime.strptime(DATE_OF_INTEREST, "%Y-%m-%d").date()
 
-# Note: this is a hack on setting the values. It should be set differently once
-# https://github.com/apache/datafusion-python/issues/665 is resolved.
-interval = pa.scalar((0, 0, INTERVAL_DAYS), type=pa.month_day_nano_interval())
+interval = pa.scalar((0, INTERVAL_DAYS, 0), type=pa.month_day_nano_interval())
 
 # Load the dataframes we need
 
diff --git a/examples/tpch/q10_returned_item_reporting.py b/examples/tpch/q10_returned_item_reporting.py
index ed88c299..78327c3a 100644
--- a/examples/tpch/q10_returned_item_reporting.py
+++ b/examples/tpch/q10_returned_item_reporting.py
@@ -38,9 +38,7 @@
 
 date_start_of_quarter = lit(datetime.strptime(DATE_START_OF_QUARTER, "%Y-%m-%d").date())
 
-# Note: this is a hack on setting the values. It should be set differently once
-# https://github.com/apache/datafusion-python/issues/665 is resolved.
-interval_one_quarter = lit(pa.scalar((0, 0, 92), type=pa.month_day_nano_interval()))
+interval_one_quarter = lit(pa.scalar((0, 92, 0), type=pa.month_day_nano_interval()))
 
 # Load the dataframes we need
 
diff --git a/examples/tpch/q12_ship_mode_order_priority.py b/examples/tpch/q12_ship_mode_order_priority.py
index d3dd7d28..150870c6 100644
--- a/examples/tpch/q12_ship_mode_order_priority.py
+++ b/examples/tpch/q12_ship_mode_order_priority.py
@@ -51,9 +51,7 @@
 
 date = datetime.strptime(DATE_OF_INTEREST, "%Y-%m-%d").date()
 
-# Note: this is a hack on setting the values. It should be set differently once
-# https://github.com/apache/datafusion-python/issues/665 is resolved.
-interval = pa.scalar((0, 0, 365), type=pa.month_day_nano_interval())
+interval = pa.scalar((0, 365, 0), type=pa.month_day_nano_interval())
 
 
 df = df_lineitem.filter(col("l_receiptdate") >= lit(date)).filter(
diff --git a/examples/tpch/q14_promotion_effect.py b/examples/tpch/q14_promotion_effect.py
index 333398c1..75fa363a 100644
--- a/examples/tpch/q14_promotion_effect.py
+++ b/examples/tpch/q14_promotion_effect.py
@@ -34,9 +34,8 @@
 DATE = "1995-09-01"
 
 date_of_interest = lit(datetime.strptime(DATE, "%Y-%m-%d").date())
-# Note: this is a hack on setting the values. It should be set differently once
-# https://github.com/apache/datafusion-python/issues/665 is resolved.
-interval_one_month = lit(pa.scalar((0, 0, 30), type=pa.month_day_nano_interval()))
+
+interval_one_month = lit(pa.scalar((0, 30, 0), type=pa.month_day_nano_interval()))
 
 # Load the dataframes we need
 
diff --git a/examples/tpch/q15_top_supplier.py b/examples/tpch/q15_top_supplier.py
index 91af34a9..4b9e4c1d 100644
--- a/examples/tpch/q15_top_supplier.py
+++ b/examples/tpch/q15_top_supplier.py
@@ -34,9 +34,8 @@
 DATE = "1996-01-01"
 
 date_of_interest = lit(datetime.strptime(DATE, "%Y-%m-%d").date())
-# Note: this is a hack on setting the values. It should be set differently once
-# https://github.com/apache/datafusion-python/issues/665 is resolved.
-interval_3_months = lit(pa.scalar((0, 0, 91), type=pa.month_day_nano_interval()))
+
+interval_3_months = lit(pa.scalar((0, 91, 0), type=pa.month_day_nano_interval()))
 
 # Load the dataframes we need
 
diff --git a/examples/tpch/q20_potential_part_promotion.py b/examples/tpch/q20_potential_part_promotion.py
index 4a602846..85e7226f 100644
--- a/examples/tpch/q20_potential_part_promotion.py
+++ b/examples/tpch/q20_potential_part_promotion.py
@@ -56,9 +56,7 @@
 
 date = datetime.strptime(DATE_OF_INTEREST, "%Y-%m-%d").date()
 
-# Note: this is a hack on setting the values. It should be set differently once
-# https://github.com/apache/datafusion-python/issues/665 is resolved.
-interval = pa.scalar((0, 0, 365), type=pa.month_day_nano_interval())
+interval = pa.scalar((0, 365, 0), type=pa.month_day_nano_interval())
 
 # Filter down dataframes
 df_nation = df_nation.filter(col("n_name") == lit(NATION_OF_INTEREST))