diff --git a/src/dynamic_foraging_processing/pipeline/_pipeline.py b/src/dynamic_foraging_processing/pipeline/_pipeline.py
index 2a08af9..00390f5 100644
--- a/src/dynamic_foraging_processing/pipeline/_pipeline.py
+++ b/src/dynamic_foraging_processing/pipeline/_pipeline.py
@@ -59,10 +59,11 @@
 #: ``TimeIntervals`` requires both, so they are derived here.
 _NWB_START_COLUMN = "quiescent_start_time"
 
-#: Columns NWB's required native ``stop_time`` is taken from, in order of
-#: preference: the end of the ITI, falling back to its start on the last trial
-#: of the session (where the ITI end is unknown).
-_NWB_STOP_COLUMNS = ("ITI_stop_time", "ITI_start_time")
+#: Trials-table column NWB's required native ``stop_time`` is taken from: the
+#: end of the ITI. This is ``NaN`` on the last trial of the session (whose ITI
+#: end is unknown) and the ``NaN`` is propagated rather than substituted, so an
+#: unknown trial end reads as unknown instead of as a shortened trial.
+_NWB_STOP_COLUMN = "ITI_stop_time"
 
 #: Source repository recorded in the ``processing.json`` data process.
 _CODE_URL = "https://github.com/AllenNeuralDynamics/dynamic-foraging-processing"
@@ -236,8 +237,9 @@ def _trial_extent(row: pd.Series) -> t.Tuple[float, float]:
 
         The trials table has no trial start/stop columns of its own, so the
         trial's extent is taken from its period bounds: it starts with the
-        quiescent period and ends with the ITI, falling back to the ITI start on
-        the last trial of the session (whose ITI end is unknown).
+        quiescent period and ends with the ITI. The last trial of the session has
+        no ITI end, so its stop time is ``NaN`` — an unknown end is reported as
+        unknown rather than substituted with an earlier landmark.
 
         Parameters
         ----------
@@ -247,11 +249,10 @@ def _trial_extent(row: pd.Series) -> t.Tuple[float, float]:
         Returns
         -------
         tuple of (float, float)
-            The trial start and stop time (seconds).
+            The trial start and stop time (seconds); the stop time is ``NaN``
+            where the ITI end is unknown.
         """
-        stops = [row[column] for column in _NWB_STOP_COLUMNS if pd.notnull(row[column])]
-        stop = stops[0] if stops else np.nan
-        return float(row[_NWB_START_COLUMN]), float(stop)
+        return float(row[_NWB_START_COLUMN]), float(row[_NWB_STOP_COLUMN])
 
     @classmethod
     def _add_trials(cls, nwb_file: pynwb.NWBFile, trials: pd.DataFrame) -> None:
@@ -265,7 +266,7 @@ def _add_trials(cls, nwb_file: pynwb.NWBFile, trials: pd.DataFrame) -> None:
         (named ``id``) is replicated as each trial's NWB ``id``. An empty table
         (or one missing the period columns the extent is derived from) is skipped.
         """
-        required = (_NWB_START_COLUMN, *_NWB_STOP_COLUMNS)
+        required = (_NWB_START_COLUMN, _NWB_STOP_COLUMN)
         if trials.empty or any(col not in trials.columns for col in required):
             return
         descriptions = TrialConfig.column_descriptions()
diff --git a/tests/test_pipeline/test_pipeline.py b/tests/test_pipeline/test_pipeline.py
index c897ea7..d7650d3 100644
--- a/tests/test_pipeline/test_pipeline.py
+++ b/tests/test_pipeline/test_pipeline.py
@@ -297,7 +297,7 @@ def test_add_trials_derives_native_start_and_stop_from_periods():
     """NWB's native trial extent spans the quiescent start to the ITI end.
 
     The last trial has no ITI end (no following quiescent period), so its stop
-    time falls back to the ITI start.
+    time stays ``NaN`` rather than falling back to an earlier landmark.
     """
     nwb_file = MagicMock()
 
@@ -307,7 +307,8 @@ def test_add_trials_derives_native_start_and_stop_from_periods():
         (call.kwargs["start_time"], call.kwargs["stop_time"])
         for call in nwb_file.add_trial.call_args_list
     ]
-    assert extents == [(0.0, 1.0), (1.0, 1.4)]
+    assert extents[0] == (0.0, 1.0)
+    assert extents[1][0] == 1.0 and np.isnan(extents[1][1])
 
 
 def test_add_trials_skips_frame_without_period_columns():