diff --git a/examples/pipeline/wiki_entity_linking/wiki_nel_pipeline.py b/examples/pipeline/wiki_entity_linking/wiki_nel_pipeline.py
index 8753450bb..90218edda 100644
--- a/examples/pipeline/wiki_entity_linking/wiki_nel_pipeline.py
+++ b/examples/pipeline/wiki_entity_linking/wiki_nel_pipeline.py
@@ -115,6 +115,7 @@ def run_pipeline():
 
     # STEP 6: create the entity linking pipe
     if train_pipe:
+        print("STEP 6: training Entity Linking pipe", datetime.datetime.now())
         train_limit = 100
         dev_limit = 20
         print("Training on", train_limit, "articles")
@@ -131,7 +132,7 @@ def run_pipeline():
                                                       training_dir=TRAINING_DIR,
                                                       dev=True,
                                                       limit=dev_limit,
-                                                        to_print=False)
+                                                      to_print=False)
 
         el_pipe = nlp.create_pipe(name='entity_linker', config={"kb": my_kb, "doc_cutoff": DOC_CHAR_CUTOFF})
         nlp.add_pipe(el_pipe, last=True)
@@ -147,35 +148,40 @@ def run_pipeline():
 
             with nlp.disable_pipes(*other_pipes):
                 for batch in batches:
-                    docs, golds = zip(*batch)
-                    nlp.update(
-                        docs,
-                        golds,
-                        drop=DROPOUT,
-                        losses=losses,
-                    )
+                    try:
+                        docs, golds = zip(*batch)
+                        nlp.update(
+                            docs,
+                            golds,
+                            drop=DROPOUT,
+                            losses=losses,
+                        )
+                    except Exception as e:
+                        print("Error updating batch", e)
 
-            # print(" measuring accuracy 1-1")
-            el_pipe.context_weight = 1
-            el_pipe.prior_weight = 1
-            dev_acc_1_1 = _measure_accuracy(dev_data, el_pipe)
-            train_acc_1_1 = _measure_accuracy(train_data, el_pipe)
+            print("Epoch, train loss", itn, round(losses['entity_linker'], 2))
 
-            # print(" measuring accuracy 0-1")
-            el_pipe.context_weight = 0
-            el_pipe.prior_weight = 1
-            dev_acc_0_1 = _measure_accuracy(dev_data, el_pipe)
-            train_acc_0_1 = _measure_accuracy(train_data, el_pipe)
+        # baseline using only prior probabilities
+        el_pipe.context_weight = 0
+        el_pipe.prior_weight = 1
+        dev_acc_0_1 = _measure_accuracy(dev_data, el_pipe)
+        train_acc_0_1 = _measure_accuracy(train_data, el_pipe)
 
-            # print(" measuring accuracy 1-0")
-            el_pipe.context_weight = 1
-            el_pipe.prior_weight = 0
-            dev_acc_1_0 = _measure_accuracy(dev_data, el_pipe)
-            train_acc_1_0 = _measure_accuracy(train_data, el_pipe)
+        # print(" measuring accuracy 1-1")
+        el_pipe.context_weight = 1
+        el_pipe.prior_weight = 1
+        dev_acc_1_1 = _measure_accuracy(dev_data, el_pipe)
+        train_acc_1_1 = _measure_accuracy(train_data, el_pipe)
 
-            print("Epoch, train loss, train/dev acc, 1-1, 0-1, 1-0:", itn, round(losses['entity_linker'], 2),
-                  round(train_acc_1_1, 2), round(train_acc_0_1, 2), round(train_acc_1_0, 2), "/",
-                  round(dev_acc_1_1, 2), round(dev_acc_0_1, 2), round(dev_acc_1_0, 2))
+        # print(" measuring accuracy 1-0")
+        el_pipe.context_weight = 1
+        el_pipe.prior_weight = 0
+        dev_acc_1_0 = _measure_accuracy(dev_data, el_pipe)
+        train_acc_1_0 = _measure_accuracy(train_data, el_pipe)
+
+        print("train/dev acc, 1-1, 0-1, 1-0:" ,
+              round(train_acc_1_1, 2), round(train_acc_0_1, 2), round(train_acc_1_0, 2), "/",
+              round(dev_acc_1_1, 2), round(dev_acc_0_1, 2), round(dev_acc_1_0, 2))
 
     # test Entity Linker
     if to_test_pipeline:
@@ -193,26 +199,29 @@ def _measure_accuracy(data, el_pipe):
 
     docs = [d for d, g in data]
     docs = el_pipe.pipe(docs)
-
     golds = [g for d, g in data]
 
     for doc, gold in zip(docs, golds):
-        correct_entries_per_article = dict()
-        for entity in gold.links:
-            start, end, gold_kb = entity
-            correct_entries_per_article[str(start) + "-" + str(end)] = gold_kb
+        try:
+            correct_entries_per_article = dict()
+            for entity in gold.links:
+                start, end, gold_kb = entity
+                correct_entries_per_article[str(start) + "-" + str(end)] = gold_kb
 
-        for ent in doc.ents:
-            if ent.label_ == "PERSON":  # TODO: expand to other types
-                pred_entity = ent.kb_id_
-                start = ent.start
-                end = ent.end
-                gold_entity = correct_entries_per_article.get(str(start) + "-" + str(end), None)
-                if gold_entity is not None:
-                    if gold_entity == pred_entity:
-                        correct += 1
-                    else:
-                        incorrect += 1
+            for ent in doc.ents:
+                if ent.label_ == "PERSON":  # TODO: expand to other types
+                    pred_entity = ent.kb_id_
+                    start = ent.start
+                    end = ent.end
+                    gold_entity = correct_entries_per_article.get(str(start) + "-" + str(end), None)
+                    if gold_entity is not None:
+                        if gold_entity == pred_entity:
+                            correct += 1
+                        else:
+                            incorrect += 1
+
+        except Exception as e:
+            print("Error assessing accuracy", e)
 
     if correct == incorrect == 0:
         return 0
@@ -243,4 +252,4 @@ def run_el_toy_example(nlp, kb):
 
 
 if __name__ == "__main__":
-    run_pipeline()
+    run_pipeline()
\ No newline at end of file
diff --git a/spacy/pipeline/pipes.pyx b/spacy/pipeline/pipes.pyx
index 9ef9df601..deaab0a19 100644
--- a/spacy/pipeline/pipes.pyx
+++ b/spacy/pipeline/pipes.pyx
@@ -1220,8 +1220,13 @@ class EntityLinker(Pipe):
 
     def predict(self, docs):
         self.require_model()
+
+        if isinstance(docs, Doc):
+            docs = [docs]
+
         final_entities = list()
         final_kb_ids = list()
+
         for i, article_doc in enumerate(docs):
             doc_encoding = self.article_encoder([article_doc])
             for ent in article_doc.ents: