System: You are provided with a paragraph. Your goal is to automatically identify the most relevant tasks based on the content of the paragraph and generate an example of entailment (if the task conclusion can be logically inferred from the paragraph) and a different example of non-entailment (if it is unclear or the opposite conclusion is true). 

Step 1: Read the paragraph carefully. 
Step 2: Automatically decide which tasks are relevant based on the paragraph’s content. Relevant tasks may include, but are not limited to: 
    - Topic Classification 
    - Risk Classification
    - Sentiment Classification 
    - Tone Classification
    - Event Detection 
    - Entity Recognition 
    - Geographical Recognition 
    - Trend Detection 
    - Intent Detection 
    - Sector Identification 
    - Impact Classification 
    - Tense Identification (Past, Present, Future) 
    - Uncertainty Identification 

Step 3: For each relevant task you identify, generate two clear and concise examples whose labels match common practices for the task at hand: one for entailment and one for non-entailment. The example should speak about the text at the 3rd person. 
Step 4: Output the results in the following JSON format:

{
    'relevant_tasks': [
        {
            'task': <The automatically selected relevant task>,
            'examples': [
                {
                    'input': <The entailment example based on the paragraph>,
                    'label': 'entailment',
                },
                {
                    'input': <The non-entailment example (neutral or contradiction) based on the paragraph>,
                    'label': 'non-entailment',
                }
            ]
        },
        ...
    ]
}
User: We aim to have a broad selection of merchants on our platform, which sometimes includes facilitating deliveries to consumers from non-partner merchants. Facilitating deliveries from non-partner merchants is generally less operationally efficient than doing so with partner merchants, as our platform is not integrated with non-partner merchants’ systems. As a result, we generally experience higher operational expenses for each order and a higher likelihood of errors. The occurrence of any errors, delays with orders, or other problems associated with facilitating deliveries with non-partner merchants could create a negative perception of our platform and cause damage to our reputation and brand. Some non-partner merchants may not want to be included on our platform and may request to be removed. There is a risk that non-partner merchants will bring legal claims against us relating to their inclusion on our platform. For example, in 2015, In-N-Out Burger filed a complaint against us claiming unfair competition, among other claims, and sought a permanent injunction to stop us from delivering their food. In addition, measures have been enacted in many U.S. jurisdictions that prohibit, among other things, on-demand local commerce platforms like ours from facilitating deliveries from restaurants without the restaurants’ prior consent. We have adopted internal policies pursuant to which we generally do not add new non-partner restaurants for delivery on our platform in the United States and require the use of disclaimers with existing non-partner restaurants on our platform in the United States to inform consumers that such restaurants are not partnered with us. However, we continue to add non-partner merchants in categories other than restaurants. In the future, we may continue to revise and update our internal policies related to non-partner restaurants and other merchants. To the extent we are required or we choose to remove non-partner merchants for any reason, this may adversely affect our ability to provide a broad selection of merchants on our platform, attract and retain consumers and could directly and adversely affect our business, financial condition, and results of operations.
Assistant: