diff --git a/spacy/kb/candidate.pxd b/spacy/kb/candidate.pxd
index ddb3dbca8..0e753bf99 100644
--- a/spacy/kb/candidate.pxd
+++ b/spacy/kb/candidate.pxd
@@ -3,13 +3,13 @@ from .kb_in_memory cimport InMemoryLookupKB
 from ..typedefs cimport hash_t
 
 cdef class Candidate:
-    cdef readonly str _entity_id_
-    cdef readonly hash_t _entity_id
-    cpdef vector[float] _entity_vector
-    cdef float _prior_prob
+    pass
 
 
 cdef class InMemoryCandidate(Candidate):
+    cdef readonly hash_t _entity_hash
+    cpdef vector[float] _entity_vector
+    cdef float _prior_prob
     cdef readonly InMemoryLookupKB _kb
     cdef float _entity_freq
     cdef hash_t _mention
diff --git a/spacy/kb/candidate.pyx b/spacy/kb/candidate.pyx
index d45d130c5..9d3a05ec8 100644
--- a/spacy/kb/candidate.pyx
+++ b/spacy/kb/candidate.pyx
@@ -12,42 +12,23 @@ cdef class Candidate:
     DOCS: https://spacy.io/api/kb/#candidate-init
     """
 
-    def __init__(
-        self,
-        entity_id: str,
-        entity_vector: vector[float],
-        prior_prob: float,
-    ):
-        """Initializes properties of abstract base class `Candidate`.
-        entity_id (Union[str, int]): Unique entity ID.
-        entity_vector (List[float]): Entity embedding.
-        prior_prob (float): Prior probability of entity for this mention - i.e. the probability that, independent of
-            the context, this mention resolves to this entity_id in the corpus used to build the knowledge base. In
-            cases in which this isn't always possible (e.g.: the corpus to analyse contains mentions that the KB corpus
-            doesn't) it might be better to eschew this information and always supply the same value.
-        """
-        # Make sure abstract KB is not instantiated.
+    def __init__(self):
+        # Make sure abstract Candidate is not instantiated.
         if self.__class__ == Candidate:
             raise TypeError(
                 Errors.E1046.format(cls_name=self.__class__.__name__)
             )
 
-        self._entity_id_ = entity_id
-        # Note that hashing an int value yields the same int value.
-        self._entity_id = hash(entity_id)
-        self._entity_vector = entity_vector
-        self._prior_prob = prior_prob
-
     @property
     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
+        raise NotImplementedError
 
     @property
     def entity_id_(self) -> str:
         """RETURNS (str): String representation of entity ID."""
-        return self._entity_id_
+        raise NotImplementedError
 
     @property
     def mention(self) -> str:
@@ -57,12 +38,12 @@ cdef class Candidate:
     @property
     def entity_vector(self) -> vector[float]:
         """RETURNS (vector[float]): Entity vector."""
-        return self._entity_vector
+        raise NotImplementedError
 
     @property
     def prior_prob(self) -> float:
         """RETURNS (List[float]): Entity vector."""
-        return self._prior_prob
+        raise NotImplementedError
 
 
 cdef class InMemoryCandidate(Candidate):
@@ -88,27 +69,36 @@ cdef class InMemoryCandidate(Candidate):
             cases in which this isn't always possible (e.g.: the corpus to analyse contains mentions that the KB corpus
             doesn't) it might be better to eschew this information and always supply the same value.
         """
-        super().__init__(
-            entity_id=kb.vocab.strings[entity_hash],
-            entity_vector=entity_vector,
-            prior_prob=prior_prob,
-        )
+        super().__init__()
+
+        self._entity_hash = entity_hash
+        self._entity_vector = entity_vector
+        self._prior_prob = prior_prob
         self._kb = kb
         self._mention = mention_hash
         self._entity_id = entity_hash
         self._entity_freq = entity_freq
 
+    @property
+    def entity_id(self) -> int:
+        return self._entity_hash
+
+    @property
+    def entity_vector(self) -> vector[float]:
+        return self._entity_vector
+
+    @property
+    def prior_prob(self) -> float:
+        return self._prior_prob
+
     @property
     def mention(self) -> str:
-        """RETURNS (str): Mention."""
         return self._kb.vocab.strings[self._mention]
 
     @property
     def entity_id_(self) -> str:
-        """RETURNS (str): ID/name of this entity in the KB."""
         return self._kb.vocab.strings[self._entity_id]
 
     @property
     def entity_freq(self) -> float:
-        """RETURNS (float): Entity frequence of this candidate's entity in the KB."""
         return self._entity_freq