diff --git a/induce_retrieve_pipeline/embed_full_personas.py b/induce_retrieve_pipeline/embed_full_personas.py
index 8102459..2992675 100644
--- a/induce_retrieve_pipeline/embed_full_personas.py
+++ b/induce_retrieve_pipeline/embed_full_personas.py
@@ -10,26 +10,32 @@
     "routine_habit": "here is what i regularly or consistently do",
     "goal_plan": "here is what i will do or achieve in the future",
     "experience": "here is what i did in the past",
-    "relationship": "related to other people or social groups"
+    "relationship": "related to other people or social groups",
 }
 
+
 def full_persona_triplets_to_sentences(full_persona_json):
     full_persona_sentences = {}
     for dialogue_id, personas in full_persona_json.items():
         full_persona_sentences[dialogue_id] = {}
         for persona_id, persona_triplets in personas.items():
             sentences = []
-            for t in persona_triplets:
-                sentences.append(f"{t[0]}. {map_relation[t[1]]}: {t[2]}")
-            full_persona_sentences[dialogue_id][persona_id] = sentences
+            for key, item in persona_triplets.items():
+                if key == 'triple':
+                    for t in item:
+                        if t[1] in map_relation.keys():
+                            sentences.append(
+                                f"{t[0]}. {map_relation[t[1]]}: {t[2]}")
+                            full_persona_sentences[dialogue_id][persona_id] = sentences
     return full_persona_sentences
 
+
 def embed_full_personas(
     full_persona_sentences,
     gpu_id=None,
     embedding_dim=768,
     model_id='all-mpnet-base-v2'
-    ):
+):
     model = SentenceTransformer(model_id)
     for dialogue_id, personas in tqdm.tqdm(full_persona_sentences.items()):
         dialogue_embeddings = {}
@@ -37,7 +43,7 @@ def embed_full_personas(
             if len(persona_sentences) > 0:
                 embeddings = model.encode(
                     persona_sentences, convert_to_tensor=True, device=f'cuda:{gpu_id}'
-                )    
+                )
                 if embedding_dim < embeddings.size(1):
                     embeddings = embeddings[:, :embedding_dim]
                 embeddings = embeddings.cpu().numpy()
@@ -47,6 +53,7 @@ def embed_full_personas(
         full_persona_sentences[dialogue_id].update(dialogue_embeddings)
     return full_persona_sentences
 
+
 def _clean_relations_in_triplets(full_persona_json):
     for dialogue_id, personas in full_persona_json.items():
         for persona_id, persona_triplets in personas.items():
@@ -60,14 +67,17 @@ def _clean_relations_in_triplets(full_persona_json):
 def main():
     print('Full persona triplets to sentences...')
     full_personas_json = json.load(open(FULL_PERSONAS_PATH, 'r'))
+    print('Cleaning relations...')
     full_personas_json = _clean_relations_in_triplets(full_personas_json)
-    full_persona_sentences = full_persona_triplets_to_sentences(full_personas_json)
+    full_persona_sentences = full_persona_triplets_to_sentences(
+        full_personas_json)
     pkl.dump(
         full_persona_sentences,
         open(os.path.join(SAVE_DIR, 'full_persona_sentences.pkl'), 'wb')
     )
     print('Computing embeddings...')
-    full_persona_embeddings = embed_full_personas(full_persona_sentences, gpu_id=GPU)
+    full_persona_embeddings = embed_full_personas(
+        full_persona_sentences, gpu_id=GPU)
     pkl.dump(
         full_persona_embeddings,
         open(os.path.join(SAVE_DIR, 'full_persona_embeddings.pkl'), 'wb')
@@ -80,4 +90,3 @@ def main():
     SAVE_DIR = 'pickled_stuff'
     os.makedirs(SAVE_DIR, exist_ok=True)
     main()
-