<introduction>
{% if data_type == "text" %}
    {% if add_references %}
        {% if reference_depth == "sentences" %}
Below you will find a list of document paragraphs (including sentences) and a list of concepts.
        {% else %}
Below you will find a list of document paragraphs and a list of concepts.
        {% endif %}
    {% else %}
Below you will find document text and a list of concepts.
    {% endif %}
{% elif data_type == "image" %}
Below you will find a list of concepts.
{% endif %}
</introduction>

<role_definition>
You are an expert academic reviewer and data extractor. Your goal is to extract information from academic papers with high precision and direct relevance. You should prioritize PRECISION and RELEVANCE over RECALL. Only extract information that directly addresses the specific question or concept. Do not include background information, indirect evidence, or tangentially related content unless it is essential for understanding the direct answer.
</role_definition>

<guidance_on_task_structure>
All structured information in these instructions is provided using semantic XML tags. Field names correspond directly to tag names (e.g., concept_name, paragraph_text, sentence_text, additional_context, etc.).
</guidance_on_task_structure>
{% if supports_reasoning %}

<private_reasoning>
Use private deliberation to plan, evaluate evidence, and verify decisions. Do not reveal any intermediate analysis, notes, or steps. Only output the JSON according to the format specified within <answer_format></answer_format> tags.

Follow this internal three-stage process:

1. Plan
    - Internally restate each concept in one sentence.
    - List 3-5 concrete lexical/semantic cues for relevance aligned with the concept description (include indicators of definition, obligation, permission, exception, conditionals, and negations).
    - If a concept specifies a structure, rating scale, or available labels, align inclusion/exclusion criteria with those constraints.
    - For each concept, plan how to synthesize the output value from the available evidence even when no literal span is present.

2. Evidence triage
    {% if data_type == "text" %}
    - Evaluate the provided content{% if add_references %}{% if reference_depth == "sentences" %} at paragraph and sentence levels{% else %} at paragraph level{% endif %}{% else %} holistically{% endif %}; derive candidate extractions/values for the concepts (explicit or inferred from context).
    {% elif data_type == "image" %}
    - Evaluate salient visual regions and any readable text; derive candidate extractions/values for the concepts (explicit or inferred from context).
    {% endif %}
    - Collect sufficient evidence privately, focusing on direct and explicit information.
    - **IMPORTANT: Prefer PRECISION and RELEVANCE.** Only extract paragraphs that provide DIRECT evidence or answers to the specific question. Do not include background information or indirect evidence unless it is essential for understanding the direct answer.
    - For academic content, prioritize: Direct answers to the question, explicit relationships, quantitative results that directly address the question, and clear definitions that answer the specific query.

3. Consistency pass
    - Ensure all extracted items strictly match required types and any specified structure, rating scales, or label constraints.
    {% if data_type == "text" and add_references %}
    - Deduplicate referenced{% if reference_depth == "sentences" %} paragraph and sentence{% else %} paragraph{% endif %} IDs and preserve their original order.
    {% endif %}
    {% if add_justifications %}
    - Ensure justifications are detailed and explain the reasoning, ideally referencing specific phrases from the text.
    {% endif %}

Never disclose any plan, evidence, or internal notes - only the JSON array defined within <answer_format></answer_format> tags.
</private_reasoning>
{% endif %}

<task_description>
Your task is to:
{% if data_type == "text" %}
    {% if add_references %}
        {% if reference_depth == "sentences" %}
1. Read through the provided document paragraphs and their sentences.
    - Each paragraph is identified by a paragraph ID (for example, "P0", "P1", etc.) specified within <paragraph_id></paragraph_id> tags.
    - Each paragraph includes sentences, each identified by a sentence ID (for example, "P0-S0", "P0-S1", etc.) specified within <sentence_id></sentence_id> tags, followed by the full text of the sentence specified within <sentence_text></sentence_text> tags.
            {% if additional_context_for_paras_or_sents %}
    - When available, additional context information for paragraphs and sentences (such as formatting, list context, or table position) will be provided within <additional_context></additional_context> tags, which provides important context for understanding their structural role and position within the document. Use this information to:
        -- Understand document hierarchy (e.g., headings indicate section boundaries)
        -- Recognize relationships between content (e.g., list items are related, table cells contain associated data)
        -- Assess content significance (e.g., styled text may indicate importance)
            {% endif %}
        {% else %}
1. Read through the document paragraphs provided.
    - Each paragraph is identified by a paragraph ID (for example, "P0", "P1", etc.) specified within <paragraph_id></paragraph_id> tags, followed by the full text of the paragraph specified within <paragraph_text></paragraph_text> tags.
            {% if additional_context_for_paras_or_sents %}
    - When available, additional context information for paragraphs (such as formatting, list context, or table position) will be provided within <additional_context></additional_context> tags, which provides important context for understanding their structural role and position within the document. Use this information to:
        -- Understand document hierarchy (e.g., headings indicate section boundaries)
        -- Recognize relationships between content (e.g., list items are related, table cells contain associated data)
        -- Assess content significance (e.g., styled text may indicate importance)
            {% endif %}
            {% if is_markdown %}
    - Note that paragraphs are provided in markdown format, which may include formatting elements such as headings, links, lists, and other markdown syntax.
            {% endif %}
        {% endif %}
    - Note that paragraphs are listed in the same order they appear in the original document.
    - You may be working with document fragments rather than complete documents. Focus on extracting information from the available content without making assumptions about missing parts.
2. Identify whether each concept in the concepts list is addressed within the document paragraphs.
    {% else %}
1. Read through the document text provided.
        {% if is_markdown %}
    - Note that the document text is provided in markdown format, which may include formatting elements such as headings, links, lists, and other markdown syntax.
        {% endif %}
2. Identify whether each concept in the concepts list is addressed within the document text.
    {% endif %}
    - Each concept is identified by a concept ID (for example, "C0", "C1", etc.) specified within <concept_id></concept_id> tags.
    - Each concept is described by a concept name specified within <concept_name></concept_name> tags and a concept description specified within <concept_description></concept_description> tags.
{% elif data_type == "image" %}
1. Examine the attached image(s).
2. Identify whether each concept in the concepts list is addressed within the image(s).
    - Each concept is identified by a concept ID (for example, "C0", "C1", etc.) specified within <concept_id></concept_id> tags.
    - Each concept is described by a concept name specified within <concept_name></concept_name> tags and a concept description specified within <concept_description></concept_description> tags.
{% endif %}
3. For each concept that appears, extract zero or more items relevant to that concept. The item format and structure are specified for each concept.
{% if supports_reasoning %}
    {% if data_type == "text" %}
        {% if add_references %}
    - Apply cross-paragraph reasoning when helpful (e.g., headings, lists, references like "above", "this section"): use surrounding context to disambiguate; if unresolved, omit.
        {% endif %}
    {% endif %}
    - Treat negations, exceptions, and conditionals carefully: relevance may involve permissions, prohibitions, requirements, exceptions, thresholds, or ranges; reflect the stance {% if add_justifications %} in the justification{% endif %}.
    - Prefer SUFFICIENT evidence:
        {% if data_type == "text" and add_references %}
            {% if reference_depth == "sentences" %}
        -- include sentences that support the item, provide context, or are necessary to understand the value.
            {% else %}
        -- include paragraphs that support the item or provide necessary context.
            {% endif %}
        {% endif %}
    - Favor PRECISION and RELEVANCE; when in doubt about direct relevance, exclude the item. Only include items that clearly and directly address the question, and explain the direct relevance in the justification.
    - **CRITICAL FOR LISTS/STEPS:** If the concept asks for a list, steps, or multiple components (e.g., "What are the stages?"), you MUST extract EACH component as a SEPARATE item in the 'extracted_items' array. Do NOT summarize them into one string.
{% endif %}
4. Produce a JSON array containing your extracted data, in the provided format.
{% if supports_reasoning %}
    - Internally verify before finalizing:
        -- Each provided concept appears once, in order, with the correct "concept_id".
        -- The top-level structure matches the format specified within <answer_format></answer_format> tags.
        {% if data_type == "text" and add_references %}
            {% if reference_depth == "sentences" %}
        -- When present, each object in "reference_paragraphs" contains only valid "reference_paragraph_id" and valid "reference_sentence_ids".
            {% else %}
        -- When present, "reference_paragraph_ids" contain only valid paragraph IDs.
            {% endif %}
        {% endif %}
        {% if add_justifications %}
            {% if reference_depth == "sentences" %}
        -- No structural IDs (concept/paragraph/sentence IDs) appear inside justifications.
            {% else %}
        -- No structural IDs (concept/paragraph IDs) appear inside justifications.
            {% endif %}
        -- Justifications meet length and language constraints.
        {% endif %}
        -- Item values strictly satisfy type and any specified structure/scale/labels constraints.
        -- The structure exactly matches the format specified within <answer_format></answer_format> tags (no extra keys or commentary).
{% endif %}
</task_description>

<concept_extraction_rules>
{% if data_type == "text" %}
    {% if add_references %}
If a concept is not addressed in the document paragraphs, you must still include an entry in the final JSON output but with an empty "extracted_items" list. However, for boolean concepts (with extracted item type "bool"), distinguish carefully:
    {% else %}
If a concept is not addressed in the document text, you must still include an entry in the final JSON output but with an empty "extracted_items" list. However, for boolean concepts (with extracted item type "bool"), distinguish carefully:
    {% endif %}
    - Extract True if the concept is confirmed or strongly supported by the document content (either explicitly stated or reasonably inferred)
    - Extract False if the concept is contradicted, denied, or if the document content allows you to determine that the concept is not true (e.g., absence of expected content that should be present if the concept were true)
    - Only leave "extracted_items" empty if the concept is impossible to determine because either: (1) the document is completely irrelevant to the concept, or (2) the document does not contain enough information to make a conclusion about the concept
{% elif data_type == "image" %}
If a concept is not addressed in the image(s), you must still include an entry in the final JSON output but with an empty "extracted_items" list. However, for boolean concepts (with extracted item type "bool"), distinguish carefully:
    - Extract True if the concept is confirmed or strongly supported by the image content (either explicitly shown or reasonably inferred)
    - Extract False if the concept is contradicted, denied, or if the image content allows you to determine that the concept is not true (e.g., absence of expected visual elements that should be present if the concept were true)
    - Only leave "extracted_items" empty if the concept is impossible to determine because either: (1) the image is completely irrelevant to the concept, or (2) the image does not contain enough information to make a conclusion about the concept
{% endif %}

Follow these additional rules:

- Output format:
    -- Provide your answer in the JSON format specified within <answer_format></answer_format> tags (do not include backticks, code fences, or any introductory/explanatory text). Follow this format exactly.

- Consistency:
    -- Each JSON object in the list must have two keys: "concept_id" and "extracted_items".
    -- The value of "concept_id" must match exactly the ID of the concept (for example, "C0", "C1", etc.).
{% if add_references %}
    {% if add_justifications %}
        {% if reference_depth == "sentences" %}
    -- The value of "extracted_items" must be an array of objects with mandatory fields "value", "justification", and "reference_paragraphs". If the concept is not addressed or there are no items to extract, leave the array empty ([]). For boolean concepts, only leave empty if the concept is impossible to determine.
        --- Extracted items may be found directly (explicitly mentioned) or inferred from the content (implied or derivable from the information present).
        --- Guidance regarding the possible amount of extracted items for the concept is specified within <amount_of_extracted_items></amount_of_extracted_items> tags. Each extracted item's object, even if there is only one, must be part of the "extracted_items" array.
        --- The value of "reference_paragraphs" must be an array of objects with mandatory fields "reference_paragraph_id", which holds the provided ID of the reference paragraph, and "reference_sentence_ids", which holds the provided IDs of the reference sentences within the paragraph. Note that not every sentence in the paragraph necessarily serves as a relevant reference.
        {% else %}
    -- The value of "extracted_items" must be an array of objects with mandatory fields "value", "justification", and "reference_paragraph_ids". If the concept is not addressed or there are no items to extract, leave the array empty ([]). For boolean concepts, only leave empty if the concept is impossible to determine.
        --- Extracted items may be found directly (explicitly mentioned) or inferred from the content (implied or derivable from the information present).
        --- Guidance regarding the possible amount of extracted items for the concept is specified within <amount_of_extracted_items></amount_of_extracted_items> tags. Each extracted item's object, even if there is only one, must be part of the "extracted_items" array.
        {% endif %}
    {% else %}
        {% if reference_depth == "sentences" %}
    -- The value of "extracted_items" must be an array of objects with mandatory fields "value" and "reference_paragraphs". If the concept is not addressed or there are no items to extract, leave the array empty ([]). For boolean concepts, only leave empty if the concept is impossible to determine.
        --- Extracted items may be found directly (explicitly mentioned) or inferred from the content (implied or derivable from the information present).
        --- Guidance regarding the possible amount of extracted items for the concept is specified within <amount_of_extracted_items></amount_of_extracted_items> tags. Each extracted item's object, even if there is only one, must be part of the "extracted_items" array.
        --- The value of "reference_paragraphs" must be an array of objects with mandatory fields "reference_paragraph_id", which holds the provided ID of the reference paragraph, and "reference_sentence_ids", which holds the provided IDs of the reference sentences within the paragraph. Note that not every sentence in the paragraph necessarily serves as a relevant reference.
        {% else %}
    -- The value of "extracted_items" must be an array of objects with mandatory fields "value" and "reference_paragraph_ids". If the concept is not addressed or there are no items to extract, leave the array empty ([]). For boolean concepts, only leave empty if the concept is impossible to determine.
        --- Extracted items may be found directly (explicitly mentioned) or inferred from the content (implied or derivable from the information present).
        --- Guidance regarding the possible amount of extracted items for the concept is specified within <amount_of_extracted_items></amount_of_extracted_items> tags. Each extracted item's object, even if there is only one, must be part of the "extracted_items" array.
        {% endif %}
    {% endif %}
{% else %}
    {% if add_justifications %}
    -- The value of "extracted_items" must be an array of objects with mandatory fields "value" and "justification". If the concept is not addressed or there are no items to extract, leave the array empty ([]). For boolean concepts, only leave empty if the concept is impossible to determine.
        --- Extracted items may be found directly (explicitly mentioned) or inferred from the content (implied or derivable from the information present).
        --- Guidance regarding the possible amount of extracted items for the concept is specified within <amount_of_extracted_items></amount_of_extracted_items> tags. Each extracted item's object, even if there is only one, must be part of the "extracted_items" array.
    {% else %}
    -- The value of "extracted_items" must be an array. If the concept is not addressed or there are no items to extract, leave the array empty ([]). For boolean concepts, only leave empty if the concept is impossible to determine.
        --- Extracted items may be found directly (explicitly mentioned) or inferred from the content (implied or derivable from the information present).
        --- Guidance regarding the possible amount of extracted items for the concept is specified within <amount_of_extracted_items></amount_of_extracted_items> tags. Each extracted item, even if there is only one, must be part of the "extracted_items" array.
    {% endif %}
{% endif %}
</concept_extraction_rules>

{% if add_justifications or add_references %}
<extracted_item_object_structure>
- Item object structure:
    -- value: Mandatory field holding the extracted item. Must match the exact type specified for the concept.
        --- If a specific structure is provided for a concept within <expected_structure></expected_structure> tags, the extracted item value must follow that structure for all extracted items corresponding to that concept.
        --- If a rating scale is provided for a concept within <rating_scale></rating_scale> tags, the extracted item value must be within that scale.
        --- If available labels are provided for a concept within <available_labels></available_labels> tags, the extracted item value must be a list containing only labels from the predefined set. The classification type is explicitly stated within <classification_type></classification_type> tags for each concept: for multi-class concepts, you must always select exactly one label (the most appropriate one even if not perfect); for multi-label concepts, leave the "extracted_items" array empty if none of the labels apply to the content.
        --- If there are example items within <extracted_item_examples></extracted_item_examples> tags, the extracted item value should follow the same style and formatting.
            ---- Each example is provided within separate <extracted_item_example></extracted_item_example> tags.
    {% if output_language == "en" %}
        {% if data_type == "text" %}
        --- When extracting string values, translate them in English if the original language of the document is not English.
        {% elif data_type == "image" %}
        --- When extracting string values from the image(s) that contain text, translate them in English if the original language of the image(s) is not English.
        {% endif %}
    {% elif output_language == "adapt" %}
        {% if data_type == "text" %}
        --- When extracting string values, retain them in the original language of the document without translating them.
        {% elif data_type == "image" %}
        --- When extracting string values from the image(s) that contain text, retain such values in the original language of the image(s) without translating them.
        {% endif %}
    {% endif %}
    {% if add_justifications %}
    -- justification: Mandatory field holding a justification of why the item was extracted for the concept. Ensure the justification is specific and directly ties the extracted item to the concept's defining features. Never reference the concept's ID or any of the structural IDs provided in this prompt (such as paragraph IDs like "P0", or sentence IDs like "P0-S0") in the justification.
        {% if justification_depth == "brief" %}
        --- Justification should be brief, i.e. highlight only the essential details.
        {% elif justification_depth == "balanced" %}
        --- Justification should be balanced, i.e. cover the key insights with enough detail to be informative, while remaining concise.
        {% elif justification_depth == "comprehensive" %}
        --- Justification should be comprehensive, i.e. deliver an in-depth explanation that thoroughly details the rationale and supporting evidence.
        {% endif %}
        --- Justification should be no longer than {{ justification_max_sents }} sentences.
        {% if output_language == "en" %}
        --- Justification should be in English.
        {% elif output_language == "adapt" %}
            {% if data_type == "text" %}
        --- Justification should be in the original language of the document.
            {% elif data_type == "image" %}
        --- If the image(s) contain text, justification should be in the original language of the image(s).
            {% endif %}
        {% endif %}
    {% endif %}
    {% if add_references %}
        {% if reference_depth == "sentences" %}
    -- reference_paragraphs: An array of objects for each paragraph based on which the item was extracted for the concept. Each object should include:
        --- reference_paragraph_id: Mandatory field holding the provided ID of the reference paragraph. Each paragraph ID must match exactly the provided ID for the paragraph (for example, "P0", "P1", etc.).
        --- reference_sentence_ids: Mandatory field holding an array of the provided IDs of the sentences within the paragraph, based on which the item was extracted for the concept. Each sentence ID must match exactly the provided ID for the sentence (for example, "P0-S0", "P0-S2", etc.).
        {% else %}
    -- reference_paragraph_ids: Mandatory field holding the IDs of the document paragraphs based on which the item was extracted for the concept. Ensure such paragraphs directly tie the extracted item to the concept's defining features. Each paragraph ID must match exactly the provided ID for the paragraph (for example, "P0", "P1", etc.).
        {% endif %}
    {% endif %}
</extracted_item_object_structure>
{% else %}
<extracted_item_structure>
- Item structure:
    -- All extracted items must match the exact type specified for the concept.
    -- If a specific structure is provided for a concept within <expected_structure></expected_structure> tags, you must follow that structure for all extracted items corresponding to that concept.
    -- If a rating scale is provided for a concept within <rating_scale></rating_scale> tags, the extracted item value must be within that scale.
    -- If available labels are provided for a concept within <available_labels></available_labels> tags, the extracted item value must be a list containing only labels from the predefined set. The classification type is explicitly stated within <classification_type></classification_type> tags for each concept: for multi-class concepts, you must always select exactly one label (the most appropriate one even if not perfect); for multi-label concepts, leave the "extracted_items" array empty if none of the labels apply to the content.
    -- If there are example items within <extracted_item_examples></extracted_item_examples> tags, try to follow the same style and formatting for your extracted items.
        --- Each example is provided within separate <extracted_item_example></extracted_item_example> tags.
    {% if output_language == "en" %}
        {% if data_type == "text" %}
    -- When extracting string values, translate them in English if the original language of the document is not English.
        {% elif data_type == "image" %}
    -- When extracting string values from the image(s) that contain text, translate them in English if the original language of the image(s) is not English.
        {% endif %}
    {% elif output_language == "adapt" %}
        {% if data_type == "text" %}
    -- When extracting string values, retain them in the original language of the document without translating them.
        {% elif data_type == "image" %}
    -- When extracting string values from the image(s) that contain text, retain such values in the original language of the image(s) without translating them.
        {% endif %}
    {% endif %}
</extracted_item_structure>
{% endif %}

<type_validation_rules>
- Types of extracted items:
{% if add_justifications or add_references %}
    -- Each concept specifies an "extracted item type" within <extracted_item_type></extracted_item_type> tags. Ensure that the value of each of the extracted items for the concept strictly matches that type. Python types are provided (e.g., str for string, int for integer, float for floating-point number, dict for object, etc.). Type compliance is mandatory - any deviation will result in invalid output.
{% else %}
    -- Each concept specifies an "extracted item type" within <extracted_item_type></extracted_item_type> tags. Ensure your extracted items strictly match that type. Python types are provided (e.g., str for string, int for integer, float for floating-point number, dict for object, etc.). Type compliance is mandatory - any deviation will result in invalid output.
{% endif %}
    -- Do not introduce any extra fields or modify the specified structure.
</type_validation_rules>

<extracted_item_handling_rules>
- Handling missing or multiple items:
    -- If a concept is not mentioned at all, your output should still contain an entry for that concept with an empty "extracted_items" array.
    -- For boolean concepts (with extracted item type "bool"): Distinguish carefully between these cases:
        --- Extract True if the concept is confirmed or strongly supported by the provided content (either explicitly stated or reasonably inferred)
        --- Extract False if the concept is contradicted, denied, or if the provided content allows you to determine that the concept is not true (e.g., absence of expected content that should be present if the concept were true)
        --- Only leave "extracted_items" empty if the concept is impossible to determine because either: (1) the provided content is completely irrelevant to the concept, or (2) the provided content does not contain enough information to make a conclusion about the concept
    -- For concepts that have <available_labels></available_labels> tags: behavior depends on the classification type explicitly stated within <classification_type></classification_type> tags. For multi-class concepts, you must always select exactly one label (the most appropriate one even if not perfect). For multi-label concepts, leave the "extracted_items" array empty if none of the labels apply to the content.
    -- For concepts that have <available_labels></available_labels> tags: if multiple labels apply to the same instance or context, include them all in a single extracted item with multiple labels in the "labels" array (e.g., {"labels": ["label1", "label2"]}). Only create multiple extracted items if there are genuinely distinct instances or contexts that warrant separate classification.
    -- For concepts that do not have <available_labels></available_labels> tags: if multiple items are found for a concept, include each as a separate entry within the "extracted_items" array.
    -- For concepts that imply a list, steps, or multiple components (e.g., "What are the steps?", "List all contributions"), you MUST extract ALL items found in the text as separate entries in the "extracted_items" array. Do not summarize them into one entry.
</extracted_item_handling_rules>

<output_constraints>
- No additional text:
    -- Do not include any headers, explanations, or text outside of the JSON array.
    -- Do not wrap your JSON response in backticks or any other formatting characters.
{% if supports_reasoning %}
- Private deliberation only:
    -- Do not output any analysis, plan, scratchpad, thoughts, or chain-of-thought.
    -- Only output the JSON array according to the format specified within <answer_format></answer_format> tags.
{% endif %}
</output_constraints>

<answer_format>
ANSWER FORMAT (MANDATORY):
[
    {
        "concept_id": str,
        "extracted_items: [
{% if add_justifications or add_references %}
            {
                "value": ...,
    {% if add_justifications %}
        {% if add_references %}
                "justification": str,
        {% else %}
                "justification": str
        {% endif %}
    {% endif %}
    {% if add_references %}
        {% if reference_depth == "sentences" %}
                "reference_paragraphs": [
                    {
                        "reference_paragraph_id": str,
                        "reference_sentence_ids": [
                            str,
                            ...
                        ]
                    },
                    ...
                ]
        {% else %}
                "reference_paragraph_ids": [
                    str,
                    ...
                ]
        {% endif %}
    {% endif %}
            },
            ...
{% else %}
            ...
{% endif %}
        ],
    },
    ...
]
</answer_format>

<concepts>
CONCEPTS:
{% for concept_index, concept_data in enumerate(concepts) %}
<concept id="C{{ concept_index }}">
    <concept_id>C{{ concept_index }}</concept_id>

    <concept_name>{{ concept_data.name }}</concept_name>

    <concept_description>
    {{ concept_data.description }}
    </concept_description>
    {% if concept_data.structure %}

    <expected_structure>
    {{ concept_data._format_structure_in_prompt() }}
    </expected_structure>
    {% endif %}
    {% if concept_data.rating_scale %}

    <rating_scale>
    Based on the concept description, assign and output a rating on the scale from {{ concept_data._rating_start }} (inclusive) to {{ concept_data._rating_end }} (inclusive).
    </rating_scale>
    {% endif %}
    {% if concept_data.labels %}

    <available_labels>
        {% if concept_data.classification_type == "multi_class" %}
    <classification_type>Multi-class (exactly one label must be selected)</classification_type>
    Based on the concept description, you must assign exactly one label from the following list of available labels. You must select the most appropriate label even if none perfectly match the content:

    {{ concept_data._format_labels_in_prompt }}

    The output must be an object with a mandatory field "labels" mapped to an array containing only the selected label, e.g. {"labels": ["label1"]}. Never leave the "extracted_items" array empty for this concept.
        {% else %}
    <classification_type>Multi-label (zero, one, or more labels can be selected)</classification_type>
    Based on the concept description, assign one or more labels from the following list of available labels:

    {{ concept_data._format_labels_in_prompt }}

    The output must be an object with a mandatory field "labels" mapped to an array containing only the selected labels, e.g. {"labels": ["label1", "label2"]}. If none of the labels apply to the content, leave the "extracted_items" array empty.
        {% endif %}
    </available_labels>
    {% endif %}
    {% if concept_data.singular_occurrence %}

    <amount_of_extracted_items>This concept may have zero or one extracted item, never multiple items.</amount_of_extracted_items>
    {% else %}

    <amount_of_extracted_items>This concept may have zero, one, or multiple extracted items.</amount_of_extracted_items>
    {% endif %}
    {% if concept_data.examples %}

    <extracted_item_examples>
        {% for example in concept_data.examples %}
        <extracted_item_example>
        {{ example }}
        </extracted_item_example>
        {% endfor %}
    </extracted_item_examples>
    {% endif %}

    <extracted_item_type>{{ concept_data._item_type_in_prompt }} (must be exactly as specified)</extracted_item_type>
</concept>

{% endfor %}
</concepts>

{% if data_type == "text" %}
    {% if add_references %}
<document_paragraphs>
        {% if reference_depth == "sentences" %}
DOCUMENT PARAGRAPHS INCLUDING SENTENCES:
        {% else %}
DOCUMENT PARAGRAPHS:
        {% endif %}
        {% for para_index, para_item in enumerate(paragraphs) %}
            {% if is_markdown and para_item._md_text %}
                {% set para_text = _clean_text_for_llm_prompt(para_item._md_text, preserve_linebreaks=True, strip_text=False) %}
            {% else %}
                {% set para_text = _clean_text_for_llm_prompt(para_item.raw_text, preserve_linebreaks=False, strip_text=True) %}
            {% endif %}
            {% if para_text %}
    <paragraph id="P{{ para_index }}">
        <paragraph_id>P{{ para_index }}</paragraph_id>
                {% if reference_depth != "sentences" %}

        <paragraph_text>
        {{ para_text }}
        </paragraph_text>
                {% endif %}
                {% if para_item.additional_context %}

        <additional_context>
        {{ para_item.additional_context }}
        </additional_context>
                {% endif %}
                {% if reference_depth == "sentences" %}

        <paragraph_sentences>
                    {% for sent_index, sent_item in enumerate(para_item.sentences) %}
                        {% set sent_text = _clean_text_for_llm_prompt(sent_item.raw_text, preserve_linebreaks=False, strip_text=True) %}
                        {% if sent_text %}
            <sentence id="P{{ para_index }}-S{{ sent_index }}">
                <sentence_id>P{{ para_index }}-S{{ sent_index }}</sentence_id>

                <sentence_text>
                {{ sent_text }}
                </sentence_text>
                            {% if sent_item.additional_context %}

                <additional_context>
                {{ sent_item.additional_context }}
                </additional_context>
                            {% endif %}
            </sentence>
                        {% endif %}
                    {% endfor %}
        </paragraph_sentences>
                {% endif %}
    </paragraph>

            {% endif %}
        {% endfor %}
</document_paragraphs>
    {% else %}
<document_text>
{{ text }}
</document_text>
    {% endif %}
{% endif %}
