lets see how the hihgt works

app.py

@@ -55,7 +55,8 @@ def extract_entities(sentence):
     print(f"Original text: {sentence}")
     print("Results:", results)
     # it should look like:
-    # [{'entity': 'org.ent.pressagency.Reuters', 'score': np.float32(98.47), 'index': 78, 'text': 'Reuters', 'start': 440, 'end': 447}]
+    # [{'entity': 'org.ent.pressagency.Reuters', 'score': np.float32(98.47),
+    # 'index': 78, 'text': 'Reuters', 'start': 440, 'end': 447}]
     for entity in results:
         entity_span = (entity["start"], entity["end"])
 
@@ -66,7 +67,9 @@ def extract_entities(sentence):
                 entity["start"] : entity["end"]
             ].strip()  # Ensure we're working with the correct portion of the text
             entity["surface"] = entity_text
-            entity["entity"] = entity["type"]
+            label = f"{entity['type']} ({entity.get('title', 'No title')}, {entity.get('name', 'No name')}, {entity.get('function', 'No function')})"
+
+            entity["entity"] = label
             # print(f"Entity text: {entity}")
 
             entities.append(entity)
@@ -75,6 +78,7 @@ def extract_entities(sentence):
     # entities = sorted(entities, key=lambda x: x["start"])
     return {"text": sentence, "entities": entities}
 
+
 # Create Gradio interface
 def ner_app_interface():
     input_sentence = gr.Textbox(