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: An increase in the costs of employee wages, benefits and insurance (including workers’ compensation, general liability, property and health) at our franchised and corporate owned restaurants could arise from an increase in the federal or state minimum wage or from general economic or competitive conditions. In addition, competition for qualified employees could compel us or our franchisees to pay higher wages to attract or retain key crew members, which could result in higher labor costs and decreased profitability. As more state and local jurisdictions implement minimum wage increases, we expect that labor costs will continue to increase. For example, California recently adopted a state-wide minimum wage of $20 per hour for employees of restaurants that are part of a national fast food chain effective April 1, 2024. The increased labor costs at certain of our franchised restaurants in California could impact their profitability and the desire to open new stores or renew their franchise agreements for existing stores and result in price increases, which could impact demand for our products or lead to operational changes. Further, the California law could prompt similar legislation in other states or localities. A material increase in labor expenses, as well as increases in general operating costs such as rent and energy, could adversely affect our franchisees’ profit margins, their sales volumes and their ability to remain in business, which would adversely affect our results of operations.
Assistant: