diff --git a/examples/pipeline/wikidata_entity_linking.py b/examples/pipeline/wikidata_entity_linking.py
new file mode 100644
index 000000000..b467a5982
--- /dev/null
+++ b/examples/pipeline/wikidata_entity_linking.py
@@ -0,0 +1,94 @@
+# coding: utf-8
+from __future__ import unicode_literals
+
+"""Demonstrate how to build a knowledge base from WikiData and run an Entity Linking algorithm.
+"""
+import json
+import spacy
+import bz2
+from spacy.kb import KnowledgeBase
+
+
+def create_kb(vocab):
+    kb = KnowledgeBase(vocab=vocab)
+    _read_wikidata()
+
+    # adding entities
+    # kb.add_entity(entity=entity, prob=prob)
+
+    # adding aliases
+    # kb.add_alias(alias=alias, entities=[entity_0, entity_1, entity_2], probabilities=[0.6, 0.1, 0.2])
+
+    print()
+    print("kb size:", len(kb), kb.get_size_entities(), kb.get_size_aliases())
+
+    return kb
+
+
+def _read_wikidata():
+    """ Read the JSON wiki data """
+    # TODO remove hardcoded path
+
+    languages = {'en', 'de'}
+
+    with bz2.open('C:/Users/Sofie/Documents/data/wikidata/wikidata-20190304-all.json.bz2', mode='rb') as file:
+        line = file.readline()
+        cnt = 1
+        while line and cnt < 10:
+            clean_line = line.strip()
+            if clean_line.endswith(b","):
+                clean_line = clean_line[:-1]
+            if len(clean_line) > 1:
+                obj = json.loads(clean_line)
+                unique_id = obj["id"]
+                print(unique_id)
+
+                labels = obj["labels"]
+                if labels:
+                    for lang in languages:
+                        lang_label = labels.get(lang, None)
+                        if lang_label:
+                            print("label (" + lang + "):", lang_label["value"])
+
+                descriptions = obj["descriptions"]
+                if descriptions:
+                    for lang in languages:
+                        lang_descr = descriptions.get(lang, None)
+                        if lang_descr:
+                            print("description (" + lang + "):", lang_descr["value"])
+
+                aliases = obj["aliases"]
+                if aliases:
+                    for lang in languages:
+                        lang_aliases = aliases.get(lang, None)
+                        if lang_aliases:
+                            for item in lang_aliases:
+                                print("alias (" + lang + "):", item["value"])
+
+                print()
+            line = file.readline()
+            cnt += 1
+
+
+def add_el(kb, nlp):
+    el_pipe = nlp.create_pipe(name='entity_linker', config={"kb": kb})
+    nlp.add_pipe(el_pipe, last=True)
+
+    text = "In The Hitchhiker's Guide to the Galaxy, written by Douglas Adams, " \
+           "Douglas reminds us to always bring our towel. " \
+           "The main character in Doug's novel is called Arthur Dent."
+    doc = nlp(text)
+
+    print()
+    for token in doc:
+        print("token", token.text, token.ent_type_, token.ent_kb_id_)
+
+    print()
+    for ent in doc.ents:
+        print("ent", ent.text, ent.label_, ent.kb_id_)
+
+
+if __name__ == "__main__":
+    nlp = spacy.load('en_core_web_sm')
+    my_kb = create_kb(nlp.vocab)
+    # add_el(my_kb, nlp)