diff --git a/ctlearn/output_handler.py b/ctlearn/output_handler.py
index 84b1af62..0808b121 100644
--- a/ctlearn/output_handler.py
+++ b/ctlearn/output_handler.py
@@ -5,7 +5,7 @@
 from astropy.table import Table
 from astropy.coordinates import SkyCoord
 import astropy.units as u
-
+from ctapipe.io.pointing import PointingInterpolator
 
 def write_output(h5file, data, rest_data, reader, predictions, tasks):
     prediction_dir = h5file.replace(f'{h5file.split("/")[-1]}', "")
@@ -144,13 +144,28 @@ def write_output(h5file, data, rest_data, reader, predictions, tasks):
             # so there is only one tel_id in the file.
             # For stereo we should fix this directly in the ctapipe plugin,
             # which is currently under development.
-            for tel_id in reader.telescope_pointings:
-                tel_pointing = reader.telescope_pointings[tel_id]
+            pointing_interpolator = PointingInterpolator()
+            tel_id_int = None
+            for tel_id, pointing_table in reader.telescope_pointings.items():
+                tel_id_int = int(tel_id.replace("tel_", ""))
+                pointing_interpolator.add_table(tel_id_int, pointing_table)
+                trigger_info = reader.tel_trigger_table[reader.tel_trigger_table["tel_id"]== tel_id_int]
+            # Check if the number of predictions and trigger info match
+            # Actually this check is redundant since the dl1dh do not allow quality cuts when processing real data
+            # However, it is still good to have it here in case table are not properly filled. 
+            if len(predictions[:, 0]) != len(trigger_info):
+                raise ValueError(
+                    f"The number of predictions ({len(predictions[:, 0])}) and trigger info ({len(trigger_info)}) do not match."
+                )
+            event_id, obs_id, tel_id = [], [], []
             reco_az, reco_alt = [], []
             pointing_az, pointing_alt, time = [], [], []
             for i, (az_off, alt_off) in enumerate(zip(predictions[:, 0], predictions[:, 1])):
-                tel_az = tel_pointing[i]['azimuth'] * u.rad
-                tel_alt = tel_pointing[i]['altitude'] * u.rad
+                event_id = trigger_info[i]['event_id']
+                obs_id = trigger_info[i]['obs_id']
+                tel_id = trigger_info[i]['tel_id']
+                time = trigger_info[i]['time']
+                tel_alt, tel_az = pointing_interpolator(tel_id_int, time)
                 pointing = SkyCoord(
                     tel_az.to_value(data.drc_unit),
                     tel_alt.to_value(data.drc_unit),
@@ -165,12 +180,14 @@ def write_output(h5file, data, rest_data, reader, predictions, tasks):
                 reco_alt.append(reco_direction.alt.to_value(data.drc_unit))
                 pointing_az.append(tel_az.to_value(u.deg))
                 pointing_alt.append(tel_alt.to_value(u.deg))
-                time.append(tel_pointing[i]['time'])
+            reco["event_id"] = np.array(event_id)
+            reco["obs_id"] = np.array(obs_id)
+            reco["tel_id"] = np.array(tel_id)
+            reco["time"] = np.array(time)
             reco["reco_az"] = np.array(reco_az)
             reco["reco_alt"] = np.array(reco_alt)
             reco["pointing_az"] = np.array(pointing_az)
             reco["pointing_alt"] = np.array(pointing_alt)
-            reco["time"] = np.array(time)
             reco["reco_sep"] = np.array(predictions[:, 2])
         else:
             reco_az, reco_alt = [], []