diff --git a/rasa_nlu/extractors/spacy_entity_extractor.py b/rasa_nlu/extractors/spacy_entity_extractor.py
index 46c0e87f03b..c9f8280db35 100644
--- a/rasa_nlu/extractors/spacy_entity_extractor.py
+++ b/rasa_nlu/extractors/spacy_entity_extractor.py
@@ -20,13 +20,19 @@ class SpacyEntityExtractor(EntityExtractor):
 
     provides = ["entities"]
 
-    requires = ["spacy_doc"]
+    requires = ["spacy_nlp"]
 
     def process(self, message, **kwargs):
         # type: (Message, **Any) -> None
 
-        extracted = self.add_extractor_name(self.extract_entities(message.get("spacy_doc")))
-        message.set("entities", message.get("entities", []) + extracted, add_to_output=True)
+        # can't use the existing doc here (spacy_doc on the message)
+        # because tokens are lower cased which is bad for NER
+        spacy_nlp = kwargs.get("spacy_nlp", None)
+        doc = spacy_nlp(message.text)
+        extracted = self.add_extractor_name(self.extract_entities(doc))
+        message.set("entities",
+                    message.get("entities", []) + extracted,
+                    add_to_output=True)
 
     def extract_entities(self, doc):
         # type: (Doc) -> List[Dict[Text, Any]]
diff --git a/rasa_nlu/utils/spacy_utils.py b/rasa_nlu/utils/spacy_utils.py
index f24e08350f7..d90a147d629 100644
--- a/rasa_nlu/utils/spacy_utils.py
+++ b/rasa_nlu/utils/spacy_utils.py
@@ -71,7 +71,7 @@ def provide_context(self):
         return {"spacy_nlp": self.nlp}
 
     def train(self, training_data, config, **kwargs):
-        # type: (TrainingData) -> Dict[Text, Any]
+        # type: (TrainingData, RasaNLUConfig, **Any) -> None
 
         for example in training_data.training_examples:
             example.set("spacy_doc", self.nlp(example.text.lower()))
diff --git a/tests/base/test_extractors.py b/tests/base/test_extractors.py
index 765bd115923..1488d36543d 100644
--- a/tests/base/test_extractors.py
+++ b/tests/base/test_extractors.py
@@ -4,6 +4,7 @@
 from __future__ import print_function
 from __future__ import unicode_literals
 
+from rasa_nlu.extractors.spacy_entity_extractor import SpacyEntityExtractor
 from tests import utilities
 from rasa_nlu.training_data import TrainingData, Message
 
@@ -107,3 +108,18 @@ def test_unintentional_synonyms_capitalized(component_builder):
     ner_syn.train(TrainingData(training_examples=examples), _config)
     assert ner_syn.synonyms.get("mexican") is None
     assert ner_syn.synonyms.get("tacos") == "Mexican"
+
+
+def test_spacy_ner_extractor(spacy_nlp):
+    ext = SpacyEntityExtractor()
+    example = Message("anywhere in the West", {
+            "intent": "restaurant_search",
+            "entities": [],
+            "spacy_doc": spacy_nlp("anywhere in the west")})
+
+    ext.process(example, spacy_nlp=spacy_nlp)
+
+    assert len(example.get("entities", [])) == 1
+    assert example.get("entities")[0] == {
+        u'start': 16, u'extractor': u'ner_spacy',
+        u'end': 20, u'value': u'West', u'entity': u'LOC'}