Add dataset card with full description

README.md

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+---
+license: mit
+task_categories:
+  - graph-ml
+  - feature-extraction
+language:
+  - en
+size_categories:
+  - 1M<n<10M
+tags:
+  - knowledge-graph
+  - graph-neural-networks
+  - entity-linking
+  - relationship-extraction
+pretty_name: DropThe Entity Relationship Graph
+---
+
+# DropThe Entity Relationship Graph
+
+A large-scale entity relationship dataset containing **2.9 million typed, directional connections** between 1.8 million entities spanning entertainment, media, finance, and technology. Extracted from the [DropThe](https://dropthe.org) knowledge graph.
+
+## Dataset Description
+
+While most open knowledge graphs focus on encyclopedic facts (Wikidata) or narrow domains (MovieLens for ratings), this dataset captures **operational relationships** -- the connections that actually matter for building recommendation systems, search engines, and analytical tools. Every edge is typed, directional, and timestamped.
+
+### Link Types
+
+| Link Type | Count | Example |
+|-----------|-------|---------|
+| `acted_in` | 820,000+ | Florence Pugh -> Oppenheimer |
+| `directed` | 96,000+ | Christopher Nolan -> Oppenheimer |
+| `produced` | 145,000+ | Emma Thomas -> Oppenheimer |
+| `works_at` | 230,000+ | Tim Cook -> Apple |
+| `founded` | 18,000+ | Jensen Huang -> NVIDIA |
+| `distributed_by` | 52,000+ | Oppenheimer -> Universal Pictures |
+| `related_to` | 1,200,000+ | Bitcoin -> Ethereum (thematic) |
+| `traded_on` | 340,000+ | Bitcoin -> Binance |
+| `parent_of` | 46,000+ | Alphabet -> Google |
+
+### Graph Statistics
+
+- **Nodes**: 1,828,455 entities across 5 types (movies, series, people, companies, cryptocurrencies)
+- **Edges**: 2,947,733 typed directional links
+- **Average degree**: 3.2 connections per entity
+- **Largest connected component**: Covers 94% of all entities
+- **Diameter**: 8 hops (entertainment subgraph)
+
+### Data Format
+
+Each edge record contains:
+
+- **source_id**: Entity ID of the origin node
+- **target_id**: Entity ID of the destination node
+- **link_type**: Typed relationship label (see table above)
+- **weight**: Confidence score (0.0-1.0) based on source reliability
+- **created_at**: Timestamp of link creation
+- **source_table**: Whether the entity comes from `entities` or `geo_entities`
+
+## Intended Use
+
+This graph dataset is particularly suited for:
+
+- **Graph neural networks** -- Train GCN, GAT, or GraphSAGE models for link prediction and node classification
+- **Knowledge graph completion** -- Predict missing edges using TransE, RotatE, or other embedding methods
+- **Recommendation systems** -- Build multi-hop recommendation paths (actor -> movie -> director -> other movies)
+- **Community detection** -- Identify clusters in the entertainment industry (studios, talent agencies, franchise ecosystems)
+- **Temporal analysis** -- Study how industry relationships evolve over time using edge timestamps
+
+## Methodology
+
+Links are extracted through a combination of structured API ingestion (TMDB, Wikidata, CoinGecko), automated entity resolution using the DropThe alias system (80,000+ aliases), and a bidirectional auto-linker that ensures graph consistency. The full pipeline is described on [DropThe](https://dropthe.org).
+
+The enrichment process runs on a local PostgreSQL instance with validation gates that check referential integrity, duplicate detection, and type conformity before any edges are promoted to the production graph on [DropThe](https://dropthe.org/data/).
+
+## Sample
+
+The included `sample_edges.csv` contains 20 representative edges across multiple relationship types, demonstrating the schema and data format.
+
+## License
+
+MIT License.
+
+## Citation
+
+```
+@dataset{dropthe_graph_2026,
+  title={DropThe Entity Relationship Graph},
+  author={DropThe},
+  year={2026},
+  url={https://dropthe.org}
+}
+```