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 arrangements with strategic, development, production and supply partners and collaborators. Some of these arrangements are evidenced by memorandums of understandings and supplier onboarding arrangements, both of which will require further negotiation at later stages of development to include additional terms relating to pricing, volume and payment terms, or replacement by production or master agreements that have yet to be implemented under separately negotiated statements of work, each of which could be terminated or might not materialize into next-stage contracts or long-term contract partnership arrangements. If these arrangements are terminated or if we are unable to enter into next-stage contracts or long-term operational contracts, our business, prospects, financial condition and operating results may be materially adversely affected. Additionally, market practice suggests that contracts with auto OEMs typically require suppliers to fulfill a customer’s requirements for a particular vehicle model’s autonomous driving or ADAS features, rather than supply a set number of products. These contracts can be subject to renegotiation, which may affect product pricing, and may be terminated by our customers at any time. Therefore, even if we are successful in obtaining series production awards and the systems into which our products are built are commercialized, the discontinuation of, the loss of business with respect to, or a lack of commercial success of a particular vehicle model or technology package for which we are a significant supplier could mean that the expected sales of our products will not materialize, materially and adversely affecting our business and prospects.
Assistant: