diff --git a/website/docs/tutorials/rule-based-matcher.jade b/website/docs/tutorials/rule-based-matcher.jade
index 53d76e145..900a86a63 100644
--- a/website/docs/tutorials/rule-based-matcher.jade
+++ b/website/docs/tutorials/rule-based-matcher.jade
@@ -9,6 +9,18 @@ p.u-text-large spaCy features a rule-matching engine that operates over tokens.
 
     nlp = spacy.load('en', parser=False, entity=False)
 
+    def merge_phrases(matcher, doc, i, matches):
+        '''
+        Merge a phrase. We have to be careful here because we'll change the token indices.
+        To avoid problems, merge all the phrases once we're called on the last match.
+        '''
+        if i != len(matches)-1:
+            return None
+        # Get Span objects
+        spans = [(ent_id, label, doc[start : end]) for ent_id, label, start, end in matches]
+        for ent_id, label, span in spans:
+            span.merge(label=label, tag='NNP' if label else span.root.tag_)
+
     matcher = Matcher(nlp.vocab)
 
     matcher.add_entity(
@@ -17,6 +29,7 @@ p.u-text-large spaCy features a rule-matching engine that operates over tokens.
         acceptor=None, # Accept or modify the match
         on_match=merge_phrases # Callback to act on the matches
     )
+
     matcher.add_pattern(
         "GoogleNow", # Entity ID -- Created if doesn't exist.
         [ # The pattern is a list of *Token Specifiers*.