diff --git a/examples/pipeline/wikidata_entity_linking.py b/examples/pipeline/wikidata_entity_linking.py
index d914f033c..b57d9f541 100644
--- a/examples/pipeline/wikidata_entity_linking.py
+++ b/examples/pipeline/wikidata_entity_linking.py
@@ -73,11 +73,11 @@ def run_pipeline():
     measure_performance = True
 
     # test the EL pipe on a simple example
-    to_test_pipeline = False
+    to_test_pipeline = True
 
     # write the NLP object, read back in and test again
-    to_write_nlp = False
-    to_read_nlp = False
+    to_write_nlp = True
+    to_read_nlp = True
     test_from_file = False
 
     # STEP 1 : create prior probabilities from WP (run only once)
@@ -154,8 +154,8 @@ def run_pipeline():
             optimizer.L2 = L2
 
         # define the size (nr of entities) of training and dev set
-        train_limit = 50000
-        dev_limit = 50000
+        train_limit = 5
+        dev_limit = 5
 
         train_data = training_set_creator.read_training(nlp=nlp_2,
                                                         training_dir=TRAINING_DIR,
@@ -250,7 +250,8 @@ def run_pipeline():
             print("STEP 9: testing NLP IO", datetime.datetime.now())
             print()
             print("writing to", NLP_2_DIR)
-            nlp_2.to_disk(NLP_2_DIR)
+            with el_pipe.model.use_params(optimizer.averages) and el_pipe.model.tok2vec.use_params(el_pipe.sgd_context.averages):
+                nlp_2.to_disk(NLP_2_DIR)
             print()
 
     # verify that the IO has gone correctly
diff --git a/spacy/pipeline/pipes.pyx b/spacy/pipeline/pipes.pyx
index f1a864fcf..91f5e7044 100644
--- a/spacy/pipeline/pipes.pyx
+++ b/spacy/pipeline/pipes.pyx
@@ -1082,12 +1082,8 @@ class EntityLinker(Pipe):
     def __init__(self, **cfg):
         self.model = True
         self.kb = None
-        self.sgd_context = None
         self.cfg = dict(cfg)
-        self.context_weight = cfg.get("context_weight", 1)
-        self.prior_weight = cfg.get("prior_weight", 1)
-        self.context_width = cfg.get("context_width")
-        self.type_to_int = cfg.get("type_to_int", dict())
+        self.sgd_context = None
 
     def set_kb(self, kb):
         self.kb = kb
@@ -1112,6 +1108,7 @@ class EntityLinker(Pipe):
 
         if sgd is None:
             sgd = self.create_optimizer()
+
         return sgd
 
     def update(self, docs, golds, state=None, drop=0.0, sgd=None, losses=None):
@@ -1138,6 +1135,8 @@ class EntityLinker(Pipe):
         priors = []
         type_vectors = []
 
+        type_to_int = self.cfg.get("type_to_int", dict())
+
         for doc, gold in zip(docs, golds):
             ents_by_offset = dict()
             for ent in doc.ents:
@@ -1148,9 +1147,9 @@ class EntityLinker(Pipe):
 
                 gold_ent = ents_by_offset[str(ent.start_char) + "_" + str(ent.end_char)]
                 assert gold_ent is not None
-                type_vector = [0 for i in range(len(self.type_to_int))]
-                if len(self.type_to_int) > 0:
-                    type_vector[self.type_to_int[gold_ent.label_]] = 1
+                type_vector = [0 for i in range(len(type_to_int))]
+                if len(type_to_int) > 0:
+                    type_vector[type_to_int[gold_ent.label_]] = 1
 
                 candidates = self.kb.get_candidates(mention)
                 random.shuffle(candidates)
@@ -1162,7 +1161,7 @@ class EntityLinker(Pipe):
                     context_docs.append(doc)
                     type_vectors.append(type_vector)
 
-                    if self.prior_weight > 0:
+                    if self.cfg.get("prior_weight", 1) > 0:
                         priors.append([c.prior_prob])
                     else:
                         priors.append([0])
@@ -1187,7 +1186,7 @@ class EntityLinker(Pipe):
             loss, d_scores = self.get_loss(prediction=pred, golds=cats, docs=None)
             mention_gradient = bp_mention(d_scores, sgd=sgd)
 
-            context_gradients = [list(x[0:self.context_width]) for x in mention_gradient]
+            context_gradients = [list(x[0:self.cfg.get("context_width")]) for x in mention_gradient]
             bp_context(self.model.ops.asarray(context_gradients, dtype="float32"), sgd=self.sgd_context)
 
             if losses is not None:
@@ -1235,13 +1234,15 @@ class EntityLinker(Pipe):
         context_encodings = self.model.tok2vec(docs)
         xp = get_array_module(context_encodings)
 
+        type_to_int = self.cfg.get("type_to_int", dict())
+
         for i, doc in enumerate(docs):
             if len(doc) > 0:
                 context_encoding = context_encodings[i]
                 for ent in doc.ents:
-                    type_vector = [0 for i in range(len(self.type_to_int))]
-                    if len(self.type_to_int) > 0:
-                        type_vector[self.type_to_int[ent.label_]] = 1
+                    type_vector = [0 for i in range(len(type_to_int))]
+                    if len(type_to_int) > 0:
+                        type_vector[type_to_int[ent.label_]] = 1
 
                     candidates = self.kb.get_candidates(ent.text)
                     if candidates:
@@ -1249,10 +1250,10 @@ class EntityLinker(Pipe):
 
                         # this will set the prior probabilities to 0 (just like in training) if their weight is 0
                         prior_probs = xp.asarray([[c.prior_prob] for c in candidates])
-                        prior_probs *= self.prior_weight
+                        prior_probs *= self.cfg.get("prior_weight", 1)
                         scores = prior_probs
 
-                        if self.context_weight > 0:
+                        if self.cfg.get("context_weight", 1) > 0:
                             entity_encodings = xp.asarray([c.entity_vector for c in candidates])
                             assert len(entity_encodings) == len(prior_probs)
                             mention_encodings = [list(context_encoding) + list(entity_encodings[i])