diff --git a/spacy/pipeline/pipes.pyx b/spacy/pipeline/pipes.pyx
index 7c800eed8..536c2a8a5 100644
--- a/spacy/pipeline/pipes.pyx
+++ b/spacy/pipeline/pipes.pyx
@@ -1219,13 +1219,11 @@ class EntityLinker(Pipe):
                         sent_doc = doc[start_token:end_token].as_doc()
                         sentence_docs.append(sent_doc)
 
-        sentence_encodings, bp_context = self.model.begin_update(sentence_docs, drop=drop)
-        loss, d_scores = self.get_similarity_loss(scores=sentence_encodings, golds=golds, docs=None)
-        bp_context(d_scores, sgd=sgd)
         set_dropout_rate(self.model, drop)
         sentence_encodings, bp_context = self.model.begin_update(sentence_docs)
         loss, d_scores = self.get_similarity_loss(scores=sentence_encodings, golds=golds)
         bp_context(d_scores)
+
         if sgd is not None:
             self.model.finish_update(sgd)
 
@@ -1306,22 +1304,28 @@ class EntityLinker(Pipe):
         if isinstance(docs, Doc):
             docs = [docs]
 
-
         for i, doc in enumerate(docs):
             sentences = [s for s in doc.sents]
 
             if len(doc) > 0:
                 # Looping through each sentence and each entity
                 # This may go wrong if there are entities across sentences - which shouldn't happen normally.
-                for sent in doc.sents:
-                    sent_doc = sent.as_doc()
+                for sent_index, sent in enumerate(sentences):
+                    # get n_neightbour sentences, clipped to the length of the document
+                    start_sentence = max(0, sent_index - self.n_sents)
+                    end_sentence = min(len(sentences) -1, sent_index + self.n_sents)
+
+                    start_token = sentences[start_sentence].start
+                    end_token = sentences[end_sentence].end
+
+                    sent_doc = doc[start_token:end_token].as_doc()
                     # currently, the context is the same for each entity in a sentence (should be refined)
                     sentence_encoding = self.model.predict([sent_doc])[0]
                     xp = get_array_module(sentence_encoding)
                     sentence_encoding_t = sentence_encoding.T
                     sentence_norm = xp.linalg.norm(sentence_encoding_t)
 
-                    for ent in sent_doc.ents:
+                    for ent in sent.ents:
                         entity_count += 1
 
                         to_discard = self.cfg.get("labels_discard", [])
@@ -1337,21 +1341,11 @@ class EntityLinker(Pipe):
                                 final_kb_ids.append(self.NIL)
                                 final_tensors.append(sentence_encoding)
 
-                        sent_doc = doc[sent.start:sent.end].as_doc()
+                            elif len(candidates) == 1:
+                                # shortcut for efficiency reasons: take the 1 candidate
 
-                        # currently, the context is the same for each entity in a sentence (should be refined)
-                        sentence_encoding = self.model([sent_doc])[0]
-                        xp = get_array_module(sentence_encoding)
-                        sentence_encoding_t = sentence_encoding.T
-                        sentence_norm = xp.linalg.norm(sentence_encoding_t)
-
-                        for ent in sent.ents:
-                            entity_count += 1
-
-                            to_discard = self.cfg.get("labels_discard", [])
-                            if to_discard and ent.label_ in to_discard:
-                                # ignoring this entity - setting to NIL
-                                final_kb_ids.append(self.NIL)
+                                # TODO: thresholding
+                                final_kb_ids.append(candidates[0].entity_)
                                 final_tensors.append(sentence_encoding)
 
                             else: