Update Entity_Extraction_JSON.ipynb (#200)

examples/json_capabilities/Entity_Extraction_JSON.ipynb

@@ -130,7 +130,7 @@
       },
       "outputs": [],
       "source": [
-        "entity_recongnition_text = \"John Johnson, the CEO of the Oil Inc. and Coal Inc. companies, has unveiled plans to build a new factory in Houston, Texas.\"\n",
+        "entity_recognition_text = \"John Johnson, the CEO of the Oil Inc. and Coal Inc. companies, has unveiled plans to build a new factory in Houston, Texas.\"\n",
         "prompt = f\"\"\"\n",
         "Generate list of entities in text based on the following Python class structure:\n",
         "\n",
@@ -147,7 +147,7 @@
         "class Entities(TypedDict):\n",
         "  entities: list[Entity]\n",
         "\n",
-        "{entity_recongnition_text}\"\"\"\n",
+        "{entity_recognition_text}\"\"\"\n",
         "model = genai.GenerativeModel(model_name='gemini-1.5-flash-latest', generation_config={\"temperature\": 0})\n",
         "response = model.generate_content(prompt, generation_config={\"response_mime_type\": \"application/json\"})"
       ]
@@ -201,7 +201,7 @@
       },
       "source": [
         "## Summary\n",
-        "You have used the Gemini API to extract entities of predifined categories with their labels. You extracted every person, company, state, and country. You are not limited to these categories, as this should work with any category of your choice.\n",
+        "You have used the Gemini API to extract entities of predefined categories with their labels. You extracted every person, company, state, and country. You are not limited to these categories, as this should work with any category of your choice.\n",
         "\n",
         "Please see the other notebooks in this directory to learn more about how you can use the Gemini API for other JSON related tasks.\n"
       ]