Business Intelligence

Wikidata Entities

Wikidata is a collaboratively maintained knowledge base with more than 121 million items covering people, organizations, places, works, and concepts. It is important for entity resolution, knowledge graphs, search enrichment, and structured research.

Why entity-ID monitoring matters

Knowledge graphs, research tools, profile pages, entity-resolution systems, and AI assistants work best when they can tie retrieval to stable identifiers. Wikidata is one of the most important public linked-data systems for that kind of structured workflow.

What Problem Does It Solve?

Keyword monitoring is often noisy when entities share names, appear in multiple languages, or need to be disambiguated. Wikidata IDs give developers and analysts a cleaner way to retrieve entity-linked news and then enrich it with graph-aware applications and AI.

Who Is It For?

Developers building knowledge-graph products, entity pages, profile systems, and research tools.

Analysts and investigators monitoring companies, people, institutions, places, and topics with stable IDs.

Content teams enriching biographies, company pages, issue hubs, and topic dashboards.

AI teams that want entity-aware retrieval for research agents and assistant workflows.

Key Benefits

Retrieve news by wikidata_id instead of relying only on ambiguous text search.

Monitor companies, people, locations, institutions, products, and topics in one structured model.

Combine Wikidata retrieval with boolean search, geography, and language filters.

Use headlines, briefs, and full text according to the depth required.

Access the same entity-aware workflow through API, MCP, and the web app.

Use Cases

Knowledge-graph applications that attach recent news to entity records.

Research tools and profile pages that need clean entity monitoring.

AI assistants that resolve entities first and then retrieve relevant news.

Issue tracking and intelligence workflows centered on specific entities and relationships.

How It Works

Resolve the entity to its Wikidata ID and store that ID in your application or workflow.

Retrieve headlines, briefs, or full text for that entity and optionally add boolean filters.

Filter by language or geography, then route results into pages, dashboards, copilots, or AI agents.

AI and MCP Workflows

Use MCP so AI agents can pull news for a known entity ID before writing a briefing, profile summary, or investigation note.

Combine Wikidata retrieval with other ontologies such as exchange MICs, industries, products, and places.

Automate entity-centric research pipelines with less ambiguity and better traceability.

Frequently Asked Questions

What kinds of entities can this service support? Companies, people, places, institutions, products, topics, and many other Wikidata-linked entities.

Why use Wikidata IDs instead of names? Because IDs are much better for disambiguation and structured workflows.

Can AI agents use entity IDs directly? Yes. MCP is very useful for that pattern.

Getting Started

Start by storing the Wikidata IDs that define your target entities, watchlists, or graph nodes. Then choose headlines, briefs, or full text depending on how much detail your users need. API, MCP, and web app access are included. Each headline costs 1 credit, each brief costs 2 credits, and each full text result costs 5 credits. For entity-heavy monitoring products, the annual plan offers stronger value because you pay for only 10 months and receive 12 months of access, which means 2 months free.

Pricing

Credit-based pricing with different rates for headlines, briefs, and full-text articles.

Tier	Credits	Price	Overage Cost
Micro	200	EUR 20/mo EUR 200/yr	EUR 0.12/credit
Starter	1,000	EUR 80/mo EUR 800/yr	EUR 0.10/credit
Business	4,000	EUR 200/mo EUR 2,000/yr	EUR 0.06/credit
Enterprise	20,000	EUR 600/mo EUR 6,000/yr	EUR 0.04/credit

Pricing Explanation

Credit-based pricing with different rates for headlines, briefs, and full-text articles.

How Pricing Works

Credit Usage

Credits are consumed based on the type of content you receive:

Headlines:1 credit per article

Briefs (summaries):2 credits per article

Full Text:5 credits per article

How It Works

Each plan includes a monthly credit allowance
Credits are consumed when articles are delivered
Choose the content depth that matches your needs
Unused credits do not roll over to the next month

Plan Features Comparison

Feature	Micro	Starter	Business	Enterprise
Number of Queries	1	3	10	30
Email Recipients	1	5	20	100
Webhooks	1	3	10	20
Portfolio Companies	1	10	40	200

Pro Tip: Start with headlines to maximize your credit usage, and upgrade to briefs or full text for topics that need deeper analysis.