diff --git a/website/usage/_training/_ner.jade b/website/usage/_training/_ner.jade
index ff3101c8f..ed58c4c6f 100644
--- a/website/usage/_training/_ner.jade
+++ b/website/usage/_training/_ner.jade
@@ -24,28 +24,60 @@ p
     |  #[strong experiment on your own data] to find a solution that works best
     |  for you.
 
-+h(3, "example-new-entity-type") Example: Training an additional entity type
++h(3, "example-new-entity-type") Training an additional entity type
 
 p
-    |  This script shows how to add a new entity type to an existing pre-trained
-    |  NER model. To keep the example short and simple, only a few sentences are
+    |  This script shows how to add a new entity type #[code ANIMAL] to an
+    |  existing pre-trained NER model, or an empty #[code Language] class. To
+    |  keep the example short and simple, only a few sentences are
     |  provided as examples. In practice, you'll need many more — a few hundred
     |  would be a good start. You will also likely need to mix in examples of
     |  other entity types, which might be obtained by running the entity
     |  recognizer over unlabelled sentences, and adding their annotations to the
     |  training set.
 
-p
-    |  The actual training is performed by looping over the examples, and
-    |  calling #[+api("language#update") #[code nlp.update()]]. The
-    |  #[code update] method steps through the words of the input. At each word,
-    |  it makes a prediction. It then consults the annotations provided on the
-    |  #[+api("goldparse") #[code GoldParse]] instance, to see whether it was
-    |  right. If it was wrong, it adjusts its weights so that the correct
-    |  action will score higher next time.
-
 +github("spacy", "examples/training/train_new_entity_type.py")
 
+p Training a new entity type requires the following steps:
+
++list("numbers")
+    +item
+        |  Create #[+api("doc") #[code Doc]] and
+        |  #[+api("goldparse") #[code GoldParse]] objects for
+        |  #[strong each example in your training data].
+
+    +item
+        |  #[strong Load the model] you want to start with, or create an
+        |  #[strong empty model] using
+        |  #[+api("spacy#blank") #[code spacy.blank()]] with the ID of your
+        |  language. If you're using an existing model, make sure to disable
+        |  all other pipeline components during training using
+        |  #[+api("language#disable_pipes") #[code nlp.disable_pipes]]. This way,
+        |  you'll only be training the entity recognizer.
+
+    +item
+        |  #[strong Add the new entity label] to the entity recognizer using the
+        |  #[+api("entityrecognizer#add_label") #[code add_label]] method. You
+        |  can access the entity recognizer in the pipeline via
+        |  #[code nlp.get_pipe('ner')].
+
+    +item
+        |  #[strong Loop over] the examples and call
+        |  #[+api("language#update") #[code nlp.update]], which steps through
+        |  the words of the input. At each word, it makes a
+        |  #[strong prediction]. It then consults the annotations provided on the
+        |  #[+api("goldparse") #[code GoldParse]] instance, to see whether it was
+        |  right. If it was wrong, it adjusts its weights so that the correct
+        |  action will score higher next time.
+
+    +item
+        |  #[strong Save] the trained model using
+        |  #[+api("language#to_disk") #[code nlp.to_disk()]].
+
+    +item
+        |  #[strong Test] the model to make sure the new entity is recognized
+        |  correctly.
+
 +h(3, "example-ner-from-scratch") Example: Training an NER system from scratch
 
 p