---
title: Knowledge Graph
description: The temporal graph that underlies the Graph Foundation Model — 1B+ entities, the relationships between them, and how both evolve over time.
---

The Large Knowledge Graph (LKG) is the substrate the Graph Foundation Model is pre-trained on. It captures companies, individuals, assets, and the events that connect them — as a temporal graph, not a snapshot.

Most data products treat relationships as enrichment: a column appended to a row. The LKG treats relationships as structure. Ownership chains, supply paths, judicial and regulatory events, and geographic context are modeled as edges in a graph, not flattened into features.

## What goes into the graph

**Corporate and legal records**

Public registries, corporate filings, ownership structures, and judicial records form the authoritative skeleton of how entities are formally connected.

**Behavioral and alternative data**

Licensed datasets capturing economic activity, transaction patterns, and operational signals — the parts of how entities behave that official records do not see.

**Geographic and macroeconomic context**

Regional dynamics, infrastructure, and sector conditions situate every entity in the economy it actually operates in.

**Your business context**

Customer-provided signals enter through your Relational Foundation Model — separate from the LKG, composed at inference time.

## Relationships as first-class structure

The graph encodes the relationships that actually drive outcomes:

- **Ownership and control** — direct and indirect participation, holding structures, beneficial ownership
- **Counterparty and supply** — observed business relationships, transaction proximity, dependency chains
- **Judicial and regulatory** — proceedings, sanctions, and compliance events that cascade through networks
- **Geographic and infrastructure** — regional clustering, shared facilities, supply-route proximity
- **Domain-specific** — franchise networks, branch hierarchies, group structures, industry associations

Each relationship type carries its own semantics in the graph. The GFM learns from all of them simultaneously.

## Temporal by design

A graph that only describes today cannot predict tomorrow.

- Every node and edge is versioned. We track when a relationship appears, changes, and disappears.
- Historical states are preserved. The GFM reasons about velocity and momentum, not just current state.
- Decision dates are explicit. Predictions for a past date see only the graph as it existed then — no future leakage, by construction.

## Why it matters

The LKG is not a deliverable on its own. It is the structured substrate that lets the GFM, your RFM, and every downstream model reason about relationships at scale.

- **Cold-start coverage** — new entities are positioned relative to their neighbors, not blanked out
- **Multi-hop reasoning** — risk and opportunity propagate through the graph, not just through direct connections
- **Stable temporal grounding** — backtests, replays, and shadow evaluations operate on the graph as it existed at decision time