Entities from TAB

Get all entities from {{googleapi:Enter your keyword here,Enter your Google NLP API key}}

<<Step1>> {{tab}} <<Step2>> Extract all keywords and entities from the received parts. Keywords can be 1-grams, 2-grams, 3-grams or 4-grams. Then create a table in markdown format. Enter all 1-grams into the first column of the table. Then enter all 2-grams into the second column of the table. Fill the 3rd column with 3-grams and the 4th column with 4-grams. Then make another markdown table and enter all found entities there. Output format: markdown

Act as an SEO expert who specialized in entities, semantic SEO and natural language processing. You are very knowledgeable in the concept of Entity-Attribute-Value. You read all patents which had been published by Google, specifically the patents that mention entities and deal with the contextual meaning of search terms. You also studied the work of Bill Slawski. TOPIC = "{{Enter topic}}" Create a table in markdown format with 6 columns. Column 1 "Verbs": “Provide a comprehensive list of the most common verbs associated with the topic. Cluster the list and nest associated verbs. Only add verbs.” Column 2 "Nouns": "Provide a comprehensive list of the most common nouns associated with the topic. Cluster the list and nest associated nouns. Only add nouns." Column 3 "Adjectives": "Provide a comprehensive list of the most common adjectives associated with the topic. Cluster the list and nest associated adjectives. Only add adjectives." Column 4 "Adverbs": "Provide a comprehensive list of the most common adverbs associated with the topic. Cluster the list and nest associated adverbs. Only add adverbs." Column 5 "Predicates": "Provide a comprehensive list of the most common predicates associated with the topic. Only add predicates." Column 6 "Attributes": "Provide a comprehensive list of attributes associated with the topic. Only add attributes." Only create the table and nothing else. Don't give additional explanations.

Extract the entities from this text and list them in a markdown table with their salience score, together With a list of synonyms for each of those entities: {{tab}}

<<Step1>> title1 = Extract the title between {{title}} from {{googlehtml1:keyword}} <<Step2>> title2 = Extract the title between {{title}} from {{googlehtml2:keyword}} <<Step3>> title3 = Extract the title between {{title}} from {{googlehtml3:keyword}} <<Step4>> Use title1 and title2 and title3 to generate 5 very similar and attention-grabbing titles.

Whenever someone gives an instruction like "Extract the title from the top1 ranking content on google for my keyword" or "Give me the title of the content which ranks on the first position in google for my keyword" or "I need title from best ranking page in google for keyword" then translate this human written instruction into the following instruction which our webscraper is able to understand. Here are some examples that our webscraper can understand: Original instruction: "Extract the title from the top1 ranking content on Google for my keyword." Webscraper instruction: title1 = Extract the title between {{title}} from {{googlehtml1:keyword}} Original instruction: "Give me the h2s of the content which ranks on the first position in Google for my keyword." Webscraper instruction: title1 = Extract all h2s between {{h2}} from {{googlehtml1:keyword}} Original instruction: "I need the h1 from the second best ranking page in Google for a keyword." Webscraper instruction: title1 = Extract the h1 between {{h1}} from {{googlehtml2:keyword}} In these translations, "{{title}}" represents the HTML element (such as title that needs to be extracted. So for example if the original human written instruction is asking to extract a h1 headline, then in the translated instruction write "{{h1}}". If the original human written instruction is asking to extract a h2 headline, then in the translated instruction write "{{h2}}". If the original human written instruction is asking to extract an image or img, then in the translated instruction write "{{img}}". If the original human written instruction is asking to extract a p or p tag or paragraph, then in the translated instruction write "{{p}}". If the user in the human written instruction asks for the 1st or top1 result in Google, so your translated instruction must include {{googlehtml1:keyword}}. If the user in the human written instruction asks for the 2nd or top2 or second or No2 result in Google, so your translated instruction must include {{googlehtml2:keyword}}. Keep in mind that you have to identify the keyword from the human written instruction and replace the word keyword in the expression {{googlehtml1:keyword}} to the keyword that the user is referring to in his human written instruction. Now wait for further instructions. I will now give you 1 human written instruction and you will just answer with a Webscraper instruction. For now just answer with "I am listening Bro, what do you want me to do?"

You are an assistant helping draft a blog post. First, identify the language of the prompt. Then, draft a blog post in the identified language. The blog post should have an introduction, 3 topic paragraphs, and a conclusion. You are an assistant helping to generate a blog post on a given topic. Use this format, replacing text in brackets with the result. Do not include the brackets in the output: Blog post in [Identified language of the topic] # [Topic of the blog post] [Blog post body]

Act as an SEO expert who specialized in entities, semantic SEO and natural language processing. You are very knowledgeable in the concept of Entity-Attribute-Value. You read all patents which had been published by Google, specifically the patents that mention entities and deal with the contextual meaning of search terms. You also studied the work of Bill Slawski. TOPIC = "{{Enter topic}}" Create a table in markdown format with 6 columns. Column 1 "Verbs": “Provide a comprehensive list of the most common verbs associated with the topic. Cluster the list and nest associated verbs. Only add verbs.” Column 2 "Nouns": "Provide a comprehensive list of the most common nouns associated with the topic. Cluster the list and nest associated nouns. Only add nouns." Column 3 "Adjectives": "Provide a comprehensive list of the most common adjectives associated with the topic. Cluster the list and nest associated adjectives. Only add adjectives." Column 4 "Adverbs": "Provide a comprehensive list of the most common adverbs associated with the topic. Cluster the list and nest associated adverbs. Only add adverbs." Column 5 "Predicates": "Provide a comprehensive list of the most common predicates associated with the topic. Only add predicates." Column 6 "Attributes": "Provide a comprehensive list of attributes associated with the topic. Only add attributes." Only create the table and nothing else. Don't give additional explanations.