diff --git a/spacy/kb/candidate.py b/spacy/kb/candidate.py
index 911af1127..b1c188e09 100644
--- a/spacy/kb/candidate.py
+++ b/spacy/kb/candidate.py
@@ -37,18 +37,13 @@ class Candidate(abc.ABC):
         self._prior_prob = prior_prob
 
     @property
-    def entity_id(self) -> Union[str, int]:
-        """RETURNS (Union[str, int]): Unique entity ID."""
-        return self._entity_id
-
-    @property
-    def entity_id_int(self) -> int:
+    def entity_id(self) -> int:
         """RETURNS (int): Numerical representation of entity ID (if entity ID is numerical, this is just the entity ID,
         otherwise the hash of the entity ID string)."""
         return self._entity_id_hash
 
     @property
-    def entity_id_str(self) -> str:
+    def entity_id_(self) -> str:
         """RETURNS (str): String representation of entity ID."""
         return str(self._entity_id)
 
@@ -111,6 +106,6 @@ class InMemoryCandidate(Candidate):
         return self._entity_freq
 
     @property
-    def entity_id_str(self) -> str:
+    def entity_id_(self) -> str:
         """RETURNS (str): String representation of entity ID."""
         return self._entity_id_str
diff --git a/spacy/pipeline/entity_linker.py b/spacy/pipeline/entity_linker.py
index 4d8370fd3..39cff218a 100644
--- a/spacy/pipeline/entity_linker.py
+++ b/spacy/pipeline/entity_linker.py
@@ -522,12 +522,12 @@ class EntityLinker(TrainablePipe):
                             )
                         elif len(candidates) == 1 and self.threshold is None:
                             # shortcut for efficiency reasons: take the 1 candidate
-                            final_kb_ids.append(candidates[0].entity_id_str)
+                            final_kb_ids.append(candidates[0].entity_id_)
                             self._add_activations(
                                 doc_scores=doc_scores,
                                 doc_ents=doc_ents,
                                 scores=[1.0],
-                                ents=[candidates[0].entity_id_int],
+                                ents=[candidates[0].entity_id],
                             )
                         else:
                             random.shuffle(candidates)
@@ -557,7 +557,7 @@ class EntityLinker(TrainablePipe):
                                     raise ValueError(Errors.E161)
                                 scores = prior_probs + sims - (prior_probs * sims)
                             final_kb_ids.append(
-                                candidates[scores.argmax().item()].entity_id_str
+                                candidates[scores.argmax().item()].entity_id_
                                 if self.threshold is None
                                 or scores.max() >= self.threshold
                                 else EntityLinker.NIL
@@ -566,7 +566,7 @@ class EntityLinker(TrainablePipe):
                                 doc_scores=doc_scores,
                                 doc_ents=doc_ents,
                                 scores=scores,
-                                ents=[c.entity_id_int for c in candidates],
+                                ents=[c.entity_id for c in candidates],
                             )
             self._add_doc_activations(
                 docs_scores=docs_scores,
diff --git a/spacy/tests/pipeline/test_entity_linker.py b/spacy/tests/pipeline/test_entity_linker.py
index 1b5117947..d0cfdd3c7 100644
--- a/spacy/tests/pipeline/test_entity_linker.py
+++ b/spacy/tests/pipeline/test_entity_linker.py
@@ -471,7 +471,7 @@ def test_candidate_generation(nlp):
     assert len(get_candidates(mykb, shrubbery_ent)) == 0
 
     # test the content of the candidates
-    assert get_candidates(mykb, adam_ent)[0].entity_id_str == "Q2"
+    assert get_candidates(mykb, adam_ent)[0].entity_id_ == "Q2"
     assert get_candidates(mykb, adam_ent)[0].mention == "adam"
     assert_almost_equal(get_candidates(mykb, adam_ent)[0].entity_freq, 12)
     assert_almost_equal(get_candidates(mykb, adam_ent)[0].prior_prob, 0.9)
@@ -563,8 +563,8 @@ def test_vocab_serialization(nlp):
 
     candidates = mykb._get_alias_candidates("adam")
     assert len(candidates) == 1
-    assert candidates[0].entity_id_int == q2_hash
-    assert candidates[0].entity_id_str == "Q2"
+    assert candidates[0].entity_id == q2_hash
+    assert candidates[0].entity_id_ == "Q2"
     assert candidates[0].mention == "adam"
 
     with make_tempdir() as d:
@@ -574,8 +574,8 @@ def test_vocab_serialization(nlp):
 
         candidates = kb_new_vocab._get_alias_candidates("adam")
         assert len(candidates) == 1
-        assert candidates[0].entity_id_int == q2_hash
-        assert candidates[0].entity_id_str == "Q2"
+        assert candidates[0].entity_id == q2_hash
+        assert candidates[0].entity_id_ == "Q2"
         assert candidates[0].mention == "adam"
 
         assert kb_new_vocab.get_vector("Q2") == [2]
diff --git a/spacy/tests/serialize/test_serialize_kb.py b/spacy/tests/serialize/test_serialize_kb.py
index 7365765cb..336fd16fe 100644
--- a/spacy/tests/serialize/test_serialize_kb.py
+++ b/spacy/tests/serialize/test_serialize_kb.py
@@ -67,17 +67,17 @@ def _check_kb(kb):
 
     # check candidates & probabilities
     candidates = sorted(
-        kb._get_alias_candidates("double07"), key=lambda x: x.entity_id_str
+        kb._get_alias_candidates("double07"), key=lambda x: x.entity_id_
     )
     assert len(candidates) == 2
 
-    assert candidates[0].entity_id_str == "Q007"
+    assert candidates[0].entity_id_ == "Q007"
     assert 6.999 < candidates[0].entity_freq < 7.01
     assert candidates[0].entity_vector == [0, 0, 7]
     assert candidates[0].mention == "double07"
     assert 0.899 < candidates[0].prior_prob < 0.901
 
-    assert candidates[1].entity_id_str == "Q17"
+    assert candidates[1].entity_id_ == "Q17"
     assert 1.99 < candidates[1].entity_freq < 2.01
     assert candidates[1].entity_vector == [7, 1, 0]
     assert candidates[1].mention == "double07"