diff --git a/spacy/cli/train.py b/spacy/cli/train.py
index 6a1d74934..06e9be15b 100644
--- a/spacy/cli/train.py
+++ b/spacy/cli/train.py
@@ -335,7 +335,7 @@ def create_evaluation_callback(nlp, optimizer, corpus, cfg):
             )
         )
 
-        n_words = sum(len(ex.doc) for ex in dev_examples)
+        n_words = sum(len(ex.predicted) for ex in dev_examples)
         start_time = timer()
 
         if optimizer.averages:
@@ -466,7 +466,7 @@ def train_while_improving(
 
 def subdivide_batch(batch, accumulate_gradient):
     batch = list(batch)
-    batch.sort(key=lambda eg: len(eg.doc))
+    batch.sort(key=lambda eg: len(eg.predicted))
     sub_len = len(batch) // accumulate_gradient
     start = 0
     for i in range(accumulate_gradient):
diff --git a/spacy/gold/example.pyx b/spacy/gold/example.pyx
index e90d8a42a..7ddc59cda 100644
--- a/spacy/gold/example.pyx
+++ b/spacy/gold/example.pyx
@@ -192,10 +192,6 @@ cdef class Example:
         def __get__(self):
             return self.x.text
 
-    property doc:
-        def __get__(self):
-            return self.x
-
     def __str__(self):
         return str(self.to_dict())
 
diff --git a/spacy/language.py b/spacy/language.py
index b9a84e1bb..01a31400a 100644
--- a/spacy/language.py
+++ b/spacy/language.py
@@ -731,7 +731,7 @@ class Language(object):
             scorer = Scorer(pipeline=self.pipeline)
         if component_cfg is None:
             component_cfg = {}
-        docs = (eg.predicted for eg in examples)
+        docs = list(eg.predicted for eg in examples)
         for name, pipe in self.pipeline:
             kwargs = component_cfg.get(name, {})
             kwargs.setdefault("batch_size", batch_size)
@@ -739,7 +739,7 @@ class Language(object):
                 docs = _pipe(docs, pipe, kwargs)
             else:
                 docs = pipe.pipe(docs, **kwargs)
-        for doc, eg in zip(docs, examples):
+        for i, (doc, eg) in enumerate(zip(docs, examples)):
             if verbose:
                 print(doc)
             eg.predicted = doc
diff --git a/spacy/pipeline/pipes.pyx b/spacy/pipeline/pipes.pyx
index d95d6766a..8bdfa0941 100644
--- a/spacy/pipeline/pipes.pyx
+++ b/spacy/pipeline/pipes.pyx
@@ -1107,7 +1107,7 @@ class EntityLinker(Pipe):
 
         for eg in examples:
             kb_ids = eg.get_aligned("ENT_KB_ID", as_string=True)
-            for ent in eg.doc.ents:
+            for ent in eg.predicted.ents:
                 kb_id = kb_ids[ent.start]  # KB ID of the first token is the same as the whole span
                 if kb_id:
                     try:
@@ -1138,7 +1138,7 @@ class EntityLinker(Pipe):
         entity_encodings = []
         for eg in examples:
             kb_ids = eg.get_aligned("ENT_KB_ID", as_string=True)
-            for ent in eg.doc.ents:
+            for ent in eg.predicted.ents:
                 kb_id = kb_ids[ent.start]
                 if kb_id:
                     entity_encoding = self.kb.get_vector(kb_id)
@@ -1158,7 +1158,7 @@ class EntityLinker(Pipe):
         cats = []
         for eg in examples:
             kb_ids = eg.get_aligned("ENT_KB_ID", as_string=True)
-            for ent in eg.doc.ents:
+            for ent in eg.predicted.ents:
                 kb_id = kb_ids[ent.start]
                 if kb_id:
                     cats.append([1.0])
diff --git a/spacy/pipeline/tok2vec.py b/spacy/pipeline/tok2vec.py
index 75654145b..047cf5caa 100644
--- a/spacy/pipeline/tok2vec.py
+++ b/spacy/pipeline/tok2vec.py
@@ -93,7 +93,7 @@ class Tok2Vec(Pipe):
         """
         if losses is None:
             losses = {}
-        docs = [eg.doc for eg in examples]
+        docs = [eg.predicted for eg in examples]
         if isinstance(docs, Doc):
             docs = [docs]
         set_dropout_rate(self.model, drop)