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: If any of our food or animal feed products were to become adulterated or misbranded, we would need to recall those items and could experience product liability claims if either consumers or customers' livestock or pets were injured or were claimed to be injured as a result. A widespread product recall or a significant product liability judgment could cause our products to be unavailable for a period of time or could cause a loss of consumer or customer confidence in our products. Even if a product liability claim were unsuccessful or were not fully pursued, the negative publicity surrounding any assertion that our products caused illness or injury could adversely affect our business and reputation with existing and potential consumers and customers and our corporate and brand image. The growing use of social and digital media by consumers has greatly increased the speed and extent that information or misinformation and opinions can be shared. Negative posts or comments about us, our brands, or our products on social or digital media could seriously damage our brands and reputation. Moreover, product liability claims or liabilities might not be covered by our insurance or by any rights of indemnity or contribution that we may have against others. In addition, general public perceptions regarding the quality, safety or health risks associated with particular food or animal feed products, such as concerns regarding genetically modified crops, could reduce demand and prices for some of the products associated with our businesses. To the extent that consumer preferences evolve away from products that our members or we produce for health or other reasons, such as the growing demand for organic food products and products that are sustainably grown and made, including low-carbon grain and oilseed, and we are unable to develop or procure products that satisfy new consumer preferences, there will be decreased demand for our products, which could have a material adverse effect on our business, financial condition, liquidity, results of operations and prospects.
Assistant: