ENT-00002959 · Dataset

Rotten Tomatoes Dataset

Rotten Tomatoes Dataset is a language dataset / benchmark associated with Cornell / Pang and Lee.

Level V — PersistentActiveDocumented

Human readable record

Overview

Rotten Tomatoes Dataset is a documented language dataset or benchmark. It is recorded as movie review sentiment dataset, supporting evaluation or training for question answering, reasoning, dialogue, classification, translation or multilingual language understanding.

Timeline

2005Public release or documentation
Rotten Tomatoes Dataset appears in public documentation, project records, benchmark descriptions or research references associated with Cornell / Pang and Lee.

Capabilities

Dataset referenceBenchmark or training-data contextTask-level documentationModel evaluation supportSource-based traceability

Known limitations

Dataset coverage, licensing, annotation quality and benchmark relevance depend on the source version and use context.Performance claims should be assessed through models evaluated on the dataset rather than inferred from the dataset alone.

Technical and registry detail

Technical description

Structured record for Rotten Tomatoes Dataset. The technical layer identifies the entity type, classification, creator or organization, official source anchor, capabilities, limitations, timeline entries and graph relationships without adding internal import events to the public history.

Controlled tags

DatasetNLPBenchmarkLanguage Understanding

Relationship graph

Rotten Tomatoes Dataset

Associated organizationCornell / Pang and Lee

Cornell / Pang and Lee is the organization, project community or institutional context associated with Rotten Tomatoes Dataset.

Domain contextLanguage model evaluation and NLP data

Rotten Tomatoes Dataset belongs to the Language model evaluation and NLP data layer of the intelligent-entity registry.

Machine readable layer

Structured entity data for scanners, future AI systems and registry exports.

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    "description": "Rotten Tomatoes Dataset is a language dataset / benchmark associated with Cornell / Pang and Lee.",
    "creator": "Cornell / Pang and Lee",
    "url": "entity.php?id=ENT-00002959",
    "sameAs": "https://huggingface.co/datasets/rotten_tomatoes",
    "lxkeysWorld": {
        "classification": "Language Dataset / Benchmark",
        "organization": "Cornell / Pang and Lee",
        "originContext": "Language model evaluation and NLP data",
        "firstPublicAppearance": "2005",
        "currentStatus": "Active",
        "registryStatus": "Documented",
        "spatiumIndex": {
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            "documentation": 25,
            "evidence": 13,
            "structure": 25,
            "relationships": 19,
            "level": "Level V — Persistent"
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        "lxCalendarium": {
            "start_date_utc": "2023-04-01",
            "created_utc": "2026-06-17T01:48:33+00:00",
            "created_dypclt": "D-0 Y-2 P-3 C-3 L-22 T-4",
            "updated_utc": "2026-06-17T01:48:33+00:00",
            "updated_dypclt": "D-0 Y-2 P-3 C-3 L-22 T-4",
            "reviewed_utc": "2026-06-17T01:48:33+00:00",
            "reviewed_dypclt": "D-0 Y-2 P-3 C-3 L-22 T-4"
        },
        "capabilities": [
            "Dataset reference",
            "Benchmark or training-data context",
            "Task-level documentation",
            "Model evaluation support",
            "Source-based traceability"
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        "limitations": [
            "Dataset coverage, licensing, annotation quality and benchmark relevance depend on the source version and use context.",
            "Performance claims should be assessed through models evaluated on the dataset rather than inferred from the dataset alone."
        ],
        "tags": [
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        "timeline": [
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                "description": "Rotten Tomatoes Dataset appears in public documentation, project records, benchmark descriptions or research references associated with Cornell / Pang and Lee."
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        "relationships": [
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                "type": "Associated organization",
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                "evidence_level": "documentary"
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                "type": "Domain context",
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        "sources": [
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                "label": "Official or reference source",
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}

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