This example demonstrates a probabilistic graph model built from a small set of word-pair sentences, tracking entity counts and conditional probabilities between vertices and edges. The Go code constructs a PGM struct with vertex and edge maps, using UUIDs from `github.com/satori/go.uuid` for unique identifiers. It implements upsert operations with mutex locking to update counts and properties, then prints global and conditional probabilities for the word "what" based on edge counts relative to totals. The visualization shows how sequential word relationships are stored and queried in a directed graph structure.