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 may from time to time, in the ordinary course of business, be subject to litigation relating to matters including, but not limited to, disputes with franchisees, suppliers, employees, team members, and customers, as well as disputes over our advertising claims about our food and over our intellectual property. For example, there have been multiple purported class action lawsuits filed against us regarding the no-poaching provision our franchise agreements for BK in the U.S. and TH in Canada, regarding certain purported privacy-related concerns with respect to geo-location data and our mobile application in Canada and regarding certain disclosures to the market, including in connection with secondary offerings of our shares. Active and potential disputes with franchisees could damage our brand reputation and our relationships with our broader franchise base. Such litigation may be expensive to defend, harm our reputation and divert resources away from our operations and negatively impact our reported earnings. Also, legal proceedings against a franchisee or its affiliates by third parties, whether in the ordinary course of business or otherwise, may include claims against us by virtue of our relationship with the franchisee. We, or our business partners, may become subject to claims for infringement of intellectual property rights and we may be required to indemnify or defend our business partners from such claims. Should management’s evaluation of our current exposure to legal matters pending against us prove incorrect and such claims are successful, our exposure could exceed expectations and have a material adverse effect on our business, financial condition and results of operations. Although some losses may be covered by insurance, if there are significant losses that are not covered, or there is a delay in receiving insurance proceeds, or the proceeds are insufficient to offset our losses fully, our financial condition or results of operations may be adversely affected.
Assistant: