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Given the task to identify and classify all named entities from a text, a Multi-Category Named Entity Recognition system can be guided by a prompt such as: 'Identify and classify all named entities in the provided text into categories such as person names, locations, dates, and organizations. List each entity with its type on one line.'

For the input text: 'Is the UK really doing that badly or have travel trends shifted, post-pandemic? For Tom Jenkins, CEO of the European Tourism Organisation, it’s the latter...', the expected output would be:
1. Tom Jenkins - Person Name
2. European Tourism Organisation - Organization
3. UK - Location
4. Europe - Location
5. 2024 - Date

Example of Multi-Category Named Entity Recognition

A system is tasked with identifying and classifying named entities from the following text: 'For Tom Jenkins, CEO of the European Tourism Organisation, it’s the latter. “I think the UK is doing perfectly well but we’ll see more people going to Europe,” he says of 2024...'. The system produces the output below. Analyze the output and identify the classification error.

System Output:
- Tom Jenkins: Person
- European Tourism Organisation: Organization
- UK: Location
- 2024: Organization

You are tasked with identifying and classifying all named entities in the following text into the categories: Person, Organization, Location, and Date. List each entity and its corresponding category.

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