diff --git a/examples/pipeline/wiki_entity_linking/train_el.py b/examples/pipeline/wiki_entity_linking/train_el.py
index 2d218ed60..20a5e4428 100644
--- a/examples/pipeline/wiki_entity_linking/train_el.py
+++ b/examples/pipeline/wiki_entity_linking/train_el.py
@@ -56,20 +56,19 @@ class EL_Model:
                                                                                 entity_descr_output,
                                                                                 False,
                                                                                 trainlimit,
+                                                                                balance=True,
                                                                                 to_print=False)
 
         dev_inst, dev_pos, dev_neg, dev_texts = self._get_training_data(training_dir,
                                                                         entity_descr_output,
                                                                         True,
                                                                         devlimit,
+                                                                        balance=False,
                                                                         to_print=False)
         self._begin_training()
 
         print()
-        self._test_dev(train_inst, train_pos, train_neg, train_texts, print_string="train_random", calc_random=True)
         self._test_dev(dev_inst, dev_pos, dev_neg, dev_texts, print_string="dev_random", calc_random=True)
-        print()
-        self._test_dev(train_inst, train_pos, train_neg, train_texts, print_string="train_pre", avg=False)
         self._test_dev(dev_inst, dev_pos, dev_neg, dev_texts, print_string="dev_pre", avg=False)
 
         instance_pos_count = 0
@@ -120,9 +119,6 @@ class EL_Model:
             print()
             print("Trained on", instance_pos_count, "/", instance_neg_count, "instances pos/neg")
 
-        if self.PRINT_TRAIN:
-            self._test_dev(train_inst, train_pos, train_neg, train_texts, print_string="train_post_avg", avg=True)
-
     def _test_dev(self, instances, pos, neg, texts_by_id, print_string, avg=False, calc_random=False):
         predictions = list()
         golds = list()
@@ -290,7 +286,7 @@ class EL_Model:
         bp_doc([doc_gradient], sgd=self.sgd_article)
         bp_entity(entity_gradients, sgd=self.sgd_entity)
 
-    def _get_training_data(self, training_dir, entity_descr_output, dev, limit, to_print):
+    def _get_training_data(self, training_dir, entity_descr_output, dev, limit, balance, to_print):
         id_to_descr = kb_creator._get_id_to_description(entity_descr_output)
 
         correct_entries, incorrect_entries = training_set_creator.read_training_entities(training_output=training_dir,
@@ -324,12 +320,16 @@ class EL_Model:
                             pos_entities[article_id + "_" + mention] = descr
 
                     for mention, entity_negs in incorrect_entries[article_id].items():
+                        neg_count = 0
                         for entity_neg in entity_negs:
                             descr = id_to_descr.get(entity_neg)
                             if descr:
-                                descr_list = neg_entities.get(article_id + "_" + mention, [])
-                                descr_list.append(descr)
-                                neg_entities[article_id + "_" + mention] = descr_list
+                                # if balance, keep only 1 negative instance for each positive instance
+                                if neg_count < 1 or not balance:
+                                    descr_list = neg_entities.get(article_id + "_" + mention, [])
+                                    descr_list.append(descr)
+                                    neg_entities[article_id + "_" + mention] = descr_list
+                                    neg_count += 1
 
         if to_print:
             print()
diff --git a/examples/pipeline/wiki_entity_linking/wiki_nel_pipeline.py b/examples/pipeline/wiki_entity_linking/wiki_nel_pipeline.py
index 23c12bfe6..0927fb394 100644
--- a/examples/pipeline/wiki_entity_linking/wiki_nel_pipeline.py
+++ b/examples/pipeline/wiki_entity_linking/wiki_nel_pipeline.py
@@ -111,7 +111,7 @@ if __name__ == "__main__":
         print("STEP 6: training", datetime.datetime.now())
         my_nlp = spacy.load('en_core_web_md')
         trainer = EL_Model(kb=my_kb, nlp=my_nlp)
-        trainer.train_model(training_dir=TRAINING_DIR, entity_descr_output=ENTITY_DESCR, trainlimit=10, devlimit=10)
+        trainer.train_model(training_dir=TRAINING_DIR, entity_descr_output=ENTITY_DESCR, trainlimit=500, devlimit=20)
         print()
 
     # STEP 7: apply the EL algorithm on the dev dataset