diff --git a/pandas/_libs/tslibs/period.pyx b/pandas/_libs/tslibs/period.pyx
index eeaf472c23b60..74804336f09a3 100644
--- a/pandas/_libs/tslibs/period.pyx
+++ b/pandas/_libs/tslibs/period.pyx
@@ -2701,8 +2701,8 @@ class Period(_Period):
                 year=None, month=None, quarter=None, day=None,
                 hour=None, minute=None, second=None):
         # freq points to a tuple (base, mult);  base is one of the defined
-        # periods such as A, Q, etc. Every five minutes would be, e.g.,
-        # ('T', 5) but may be passed in as a string like '5T'
+        # periods such as Y, Q, etc. Every five minutes would be, e.g.,
+        # ('min', 5) but may be passed in as a string like '5min'
 
         # ordinal is the period offset from the gregorian proleptic epoch
 
diff --git a/pandas/core/arrays/period.py b/pandas/core/arrays/period.py
index e3492dd21ea57..4c3b2e549e49d 100644
--- a/pandas/core/arrays/period.py
+++ b/pandas/core/arrays/period.py
@@ -623,7 +623,7 @@ def to_timestamp(self, freq=None, how: str = "start") -> DatetimeArray:
         ----------
         freq : str or DateOffset, optional
             Target frequency. The default is 'D' for week or longer,
-            'S' otherwise.
+            's' otherwise.
         how : {'s', 'e', 'start', 'end'}
             Whether to use the start or end of the time period being converted.
 
diff --git a/pandas/tests/io/json/test_json_table_schema.py b/pandas/tests/io/json/test_json_table_schema.py
index 7569a74752bf2..cc101bb9c8b6d 100644
--- a/pandas/tests/io/json/test_json_table_schema.py
+++ b/pandas/tests/io/json/test_json_table_schema.py
@@ -801,7 +801,7 @@ def test_comprehensive(self):
                 "A": [1, 2, 3, 4],
                 "B": ["a", "b", "c", "c"],
                 "C": pd.date_range("2016-01-01", freq="d", periods=4),
-                # 'D': pd.timedelta_range('1H', periods=4, freq='T'),
+                # 'D': pd.timedelta_range('1h', periods=4, freq='min'),
                 "E": pd.Series(pd.Categorical(["a", "b", "c", "c"])),
                 "F": pd.Series(pd.Categorical(["a", "b", "c", "c"], ordered=True)),
                 "G": [1.1, 2.2, 3.3, 4.4],