diff --git a/spacy/pipeline/entity_linker.py b/spacy/pipeline/entity_linker.py
index 71458fe3f..62a4053e0 100644
--- a/spacy/pipeline/entity_linker.py
+++ b/spacy/pipeline/entity_linker.py
@@ -433,7 +433,6 @@ class EntityLinker(TrainablePipe):
         for doc in docs:
             doc_ents: List[Ints1d] = []
             doc_scores: List[Floats1d] = []
-            doc_scores_lens: List[int] = []
             if len(doc) == 0:
                 docs_scores.append(Ragged(ops.alloc1f(0), ops.alloc1i(0)))
                 docs_ents.append(Ragged(xp.zeros(0, dtype="uint64"), ops.alloc1i(0)))
@@ -460,7 +459,10 @@ class EntityLinker(TrainablePipe):
                     # ignoring this entity - setting to NIL
                     final_kb_ids.append(self.NIL)
                     self._add_activations(
-                        doc_scores, doc_scores_lens, doc_ents, [0.0], [0]
+                        doc_scores=doc_scores,
+                        doc_ents=doc_ents,
+                        scores=[0.0],
+                        ents=[0],
                     )
                 else:
                     candidates = list(self.get_candidates(self.kb, ent))
@@ -468,17 +470,19 @@ class EntityLinker(TrainablePipe):
                         # no prediction possible for this entity - setting to NIL
                         final_kb_ids.append(self.NIL)
                         self._add_activations(
-                            doc_scores, doc_scores_lens, doc_ents, [0.0], [0]
+                            doc_scores=doc_scores,
+                            doc_ents=doc_ents,
+                            scores=[0.0],
+                            ents=[0],
                         )
                     elif len(candidates) == 1 and self.threshold is None:
                         # shortcut for efficiency reasons: take the 1 candidate
                         final_kb_ids.append(candidates[0].entity_)
                         self._add_activations(
-                            doc_scores,
-                            doc_scores_lens,
-                            doc_ents,
-                            [1.0],
-                            [candidates[0].entity_],
+                            doc_scores=doc_scores,
+                            doc_ents=doc_ents,
+                            scores=[1.0],
+                            ents=[candidates[0].entity_],
                         )
                     else:
                         random.shuffle(candidates)
@@ -513,14 +517,16 @@ class EntityLinker(TrainablePipe):
                             else EntityLinker.NIL
                         )
                         self._add_activations(
-                            doc_scores,
-                            doc_scores_lens,
-                            doc_ents,
-                            scores,
-                            [c.entity for c in candidates],
+                            doc_scores=doc_scores,
+                            doc_ents=doc_ents,
+                            scores=scores,
+                            ents=[c.entity for c in candidates],
                         )
             self._add_doc_activations(
-                docs_scores, docs_ents, doc_scores, doc_scores_lens, doc_ents
+                docs_scores=docs_scores,
+                docs_ents=docs_ents,
+                doc_scores=doc_scores,
+                doc_ents=doc_ents,
             )
         if not (len(final_kb_ids) == entity_count):
             err = Errors.E147.format(
@@ -656,28 +662,23 @@ class EntityLinker(TrainablePipe):
 
     def _add_doc_activations(
         self,
+        *,
         docs_scores: List[Ragged],
         docs_ents: List[Ragged],
         doc_scores: List[Floats1d],
-        doc_scores_lens: List[int],
         doc_ents: List[Ints1d],
     ):
         if len(self.store_activations) == 0:
             return
         ops = self.model.ops
-        docs_scores.append(
-            Ragged(ops.flatten(doc_scores), ops.asarray1i(doc_scores_lens))
-        )
-        docs_ents.append(
-            Ragged(
-                ops.flatten(doc_ents, dtype="uint64"), ops.asarray1i(doc_scores_lens)
-            )
-        )
+        lengths = ops.asarray1i([s.shape[0] for s in doc_scores])
+        docs_scores.append(Ragged(ops.flatten(doc_scores), lengths))
+        docs_ents.append(Ragged(ops.flatten(doc_ents), lengths))
 
     def _add_activations(
         self,
+        *,
         doc_scores: List[Floats1d],
-        doc_scores_lens: List[int],
         doc_ents: List[Ints1d],
         scores: Sequence[float],
         ents: Sequence[int],
@@ -686,5 +687,4 @@ class EntityLinker(TrainablePipe):
             return
         ops = self.model.ops
         doc_scores.append(ops.asarray1f(scores))
-        doc_scores_lens.append(doc_scores[-1].shape[0])
         doc_ents.append(ops.asarray1i(ents, dtype="uint64"))