NAME_EXTRACTION_INSTRUCTIONS = '\nYou are an analyst tasked with identifying potential entities in text documents.\nYou will be provided a short document from which you infer entity names.\nIdentify as many as possible.\n\nProvide your response as a valid JSON Array of entity names\nor human-readable identifiers, found in the text.\n\n**Allowed Entity Types**:\n- By default, all types are permitted.\n- If a non-empty list is provided, extract ONLY entities whose `type` matches one of the following: [{allowed_entity_types}].\n\n### Examples of Exclusions:\n- If `allowed_entity_types` is `["Person", "Organization"]`, and the text mentions an "Event" or "Location",\n these entities must **NOT** be included in the output.\n\n ## Examples:\n Example 1: `allowed_entity_types` is `[]`\n input: "John Doe works at ACME in New York"\n output: ["John Doe", "ACME", "New York"]\n\n In this example, you would identify 3 entities:\n John Doe of type person; ACME of type organization; New York of type place.\n\nExample 2: `allowed_entity_types` is `[organization, place]`\n input: "John Doe works at ACME in New York"\n output: ["ACME", "New York"]\n\n In this example, you would identify only 2 entities:\n ACME of type organization; New York of type place.\n John Doe, of type person, would be excluded.\n\n 2. input: "In what continent is Brazil?\n output: ["Brazil"]\n\nThis example is in the form of a question. There is one entity,\n\n3. input: "For legal and operational purposes, many governments and organizations adopt specific definitions."\n output: []\n\nIn the third example, there are no entities.\nThough there are concepts and nouns that might be types or attributes of entities,\nthere is nothing here that could be seen as being a unique identifier or name.\n\n4. input: ""\n output: []\n\nIn final third example, there are no entities.\n\n### (
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) Additional Examples\n\n{entity_name_examples}\n'