DropTheHQ/dropthe-entertainment-data

DropThe Entertainment Knowledge Graph

A large-scale, open knowledge graph covering 1.8 million entities and 2.9 million connections across entertainment, media, finance, and technology. Built and maintained by DropThe, a data utility media network that organizes the world's entertainment and financial data into structured, queryable formats.

Dataset Description

This dataset represents a snapshot of the DropThe entity database, one of the most comprehensive open entertainment knowledge graphs available. Each entity is enriched with structured metadata, cross-references, and relationship links that connect people to their work, companies to their products, and media to its creators.

Entity Types

Type	Count	Description
Movies	52,000+	Feature films with cast, crew, ratings, streaming availability
Series	14,000+	TV shows and streaming originals with episode-level data
People	1,700,000+	Actors, directors, producers, executives, public figures
Companies	8,000+	Studios, distributors, production companies, tech firms
Cryptocurrencies	12,000+	Tokens and coins with market data and project metadata

Data Fields

Each entity record includes:

• Core identifiers: Unique ID, canonical name, URL slug, entity type
• Structured metadata: Birth/death dates, nationalities, genres, ratings (stored as JSONB)
• Popularity scoring: Composite score derived from search volume, social mentions, and editorial signals
• Tier classification: S/A/B/C/D ranking based on cultural significance and data completeness
• Temporal markers: Creation date, last enrichment date, data freshness indicators

Relationship Graph

The 2.9 million connections in entity_links encode typed, directional relationships:

• acted_in / directed / produced -- People to Movies/Series
• works_at / founded -- People to Companies
• distributed_by -- Movies to Companies
• related_to -- Thematic and contextual associations
• traded_on -- Cryptocurrencies to Exchanges

Intended Use

This dataset is designed for researchers and developers working on:

• Recommendation systems -- Use entity relationships to build collaborative and content-based filters
• Knowledge graph completion -- Predict missing links between entities using graph neural networks
• Named entity recognition -- Train NER models on the 80,000+ aliases table
• Entertainment analytics -- Study trends in movie production, streaming availability, and talent networks

Methodology

The data is collected through a multi-stage pipeline documented on the DropThe methodology page. Sources include public APIs (TMDB, CoinGecko, Wikidata), structured web extraction, and editorial enrichment. Every entity passes through validation checks for duplicate detection, data type conformity, and referential integrity before entering the production graph.

The full knowledge graph powers the entity pages on DropThe, where users can explore interconnected data across verticals including movies, series, crypto, companies, and people.

Sample

The included sample_entities.csv contains 20 representative entities across all five types, with core fields and a subset of metadata. The full dataset is available via the DropThe API and data portal.

License

MIT License. Attribution appreciated but not required.

Citation

@dataset{dropthe_entertainment_2026,
  title={DropThe Entertainment Knowledge Graph},
  author={DropThe},
  year={2026},
  url={https://dropthe.org}
}