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 have recalled, and could in the future recall, products due to product quality or safety issues, including actual or alleged mislabeling, misbranding, spoilage, undeclared allergens, adulteration or contamination. Joint ventures in which we have an interest have also recalled, and could in the future recall, products for the same or other reasons. Product recalls, including the voluntary recall of certain bars and cereals in our QFNA division (Quaker Recall), have in the past and could in the future adversely affect our business by resulting in losses due to their cost, the destruction of product inventory, customer fines and returns or lost sales due to any unavailability of the product for a period of time. In addition, product quality or safety issues have in the past and could in the future also reduce consumer confidence and demand for our products, cause production and delivery disruptions, including as a result of temporary or permanent closure of manufacturing plants or facilities, and result in increased costs (including payment of fines and/or judgments, cleaning and remediation costs and legal fees, and costs associated with alternative sources of production) and damage our reputation (or the reputation of joint ventures in which we have an interest), particularly as we or our joint ventures continue to expand into new categories, all of which can adversely affect our business. Any perception or allegation (whether or not valid) of failure to maintain adequate oversight over product quality or safety can result in product recalls, litigation, government investigations or inquiries or civil or criminal proceedings, all of which may result in fines, penalties, damages or criminal liability. Our business can also be adversely affected if consumers lose confidence in product quality, safety and integrity generally, even if such loss of confidence is unrelated to products in our portfolio. In addition, while we currently maintain insurance coverage that, subject to its terms and conditions, is intended to address costs associated with certain aspects of product recalls, this insurance coverage may not, depending on the specific facts and circumstances surrounding an incident, cover all losses or all types of claims that arise from an incident, or the damage to our reputation or brands that may result from an incident.
Assistant: