diff --git a/README.md b/README.md
index 6191b9f..c1c34ac 100644
--- a/README.md
+++ b/README.md
@@ -252,12 +252,16 @@ even when it was not selected into the applied profile.
 A name match never overrides value shape, even for names on the trusted
 priority lists such as `context`. If the observed values for a name-matched
 field confidently look like free text (a raw log line or URL, not a
-low-cardinality category) it is excluded from entity, categorical, and
-relationship-target selection regardless of its name, both for a fresh
-recommendation and as a second, independent check when a field reaches
-relationship-candidate selection through cross-stream field sharing. A field
-with too few samples to classify is never excluded on that basis alone; the
-veto only fires on a confident, contradicting shape.
+low-cardinality category) or like a timestamp, it is excluded from entity,
+categorical, and relationship-target selection regardless of its name, both
+for a fresh recommendation and as a second, independent check when a field
+reaches relationship-candidate selection through cross-stream field sharing.
+The timestamp case matters for the same reason free text does: a timestamp
+is near-unique on essentially every event, so tracking it as a categorical
+or relationship field is guaranteed to produce a "never seen before" finding
+every single time, exactly the failure mode that made `context` a problem.
+A field with too few samples to classify is never excluded on that basis
+alone; the veto only fires on a confident, contradicting shape.
 
 Windows-like streams are recognized from stream names such as `Windows`,
 `Winlog`, `Security Event Log`, `Sysmon`, and `Powershell`, or from Windows event
diff --git a/src/fgai/profile_suggestions.py b/src/fgai/profile_suggestions.py
index eedb913..0a82cfe 100644
--- a/src/fgai/profile_suggestions.py
+++ b/src/fgai/profile_suggestions.py
@@ -12,10 +12,10 @@ from .normalization import FIELD_ALIASES
 SHAPE_ENTITY_CONFIDENCE = 60
 
 _SHAPE_VETO = {
-    "entity": {"free_text"},
-    "categorical": {"free_text"},
+    "entity": {"free_text", "timestamp"},
+    "categorical": {"free_text", "timestamp"},
     "numeric": {"free_text", "categorical_low_cardinality"},
-    "relationship": {"free_text"},
+    "relationship": {"free_text", "timestamp"},
 }
 
 # classify_field_shape() gives free_text a fixed 0.4 fit ratio and no name
diff --git a/tests/test_profile_suggestions.py b/tests/test_profile_suggestions.py
index 0732f90..24bf587 100644
--- a/tests/test_profile_suggestions.py
+++ b/tests/test_profile_suggestions.py
@@ -393,6 +393,28 @@ class ProfileSuggestionTests(unittest.TestCase):
         self.assertNotIn("context", relationship_fields)
         self.assertIn("srcip", profile["entity_fields"])
 
+    def test_timestamp_shaped_field_is_vetoed_from_categorical_and_relationships(self):
+        events = [
+            parse_log_line(
+                f"fgai_stream_id=squid fgai_stream=Squid srcip=10.0.0.{index % 7} "
+                f"gl2_processing_finished=2026-06-29T10:{index % 60:02d}:00Z "
+                f"action=allow timestamp=2026-06-29T10:00:{index % 60:02d}Z"
+            )
+            for index in range(1, 40)
+        ]
+
+        suggestion = suggest_stream_profiles(events)[0]
+        profile = suggestion["profile"]
+
+        self.assertEqual(suggestion["field_shapes"]["gl2_processing_finished"], "timestamp")
+        self.assertNotIn("gl2_processing_finished", profile["entity_fields"])
+        self.assertNotIn("gl2_processing_finished", profile["categorical_fields"])
+        relationship_fields = {item["left"] for item in profile["relationship_fields"]} | {
+            item["right"] for item in profile["relationship_fields"]
+        }
+        self.assertNotIn("gl2_processing_finished", relationship_fields)
+        self.assertIn("srcip", profile["entity_fields"])
+
     def test_field_shape_classification_uses_real_event_volume_not_sample_size(self):
         # 24 distinct usernames repeating across 300 events is genuinely low
         # cardinality (8%) even though 24 distinct values alone would look