brandburner/startrektng-mega-narrative-kg

Star Trek: The Next Generation - Narrative Knowledge Graph

A rich narrative knowledge graph extracted from Star Trek: The Next Generation screenplays using the Fabula pipeline. Contains characters, locations, objects, organizations, events, themes, and conflict arcs with full participation semantics and Graph Gravity importance tiers.

Dataset Overview

Entity Breakdown

Graph Gravity Tiers

Relationship Types

AFFILIATED_WITH, BELONGS_TO_EPISODE, CALLBACK, CAUSAL, CHARACTER_CONTINUITY, CONTAINS_BEAT, CREDITED_ON, EMOTIONAL_ECHO, ESCALATION, EXEMPLIFIES_THEME, FORESHADOWING, INVOLVED_IN_ARC, INVOLVED_WITH, IN_EVENT, NARRATIVELY_FOLLOWS, OCCURS_IN, OWNS, PARTICIPATED_AS, PART_OF, PART_OF_ACT ... and 6 more

Megagraph vs. Season Datasets

This is a megagraph — a cross-season unified knowledge graph, not a simple concatenation of per-season datasets.

Key differences from individual season datasets:

• —Unified entity identities: Cross-season entities (recurring characters, locations, organizations) are reconciled through a Global Entity Registry (GER) and assigned new canonical UUIDs. The same character will have a different node_id here than in any individual season dataset.
• —Distilled descriptions: Entity descriptions may be rewritten during GER reconciliation to reflect a character's full arc rather than a single season's perspective.
• —Cross-season Graph Gravity: Tier assignments (anchor/planet/asteroid) reflect importance across all 177 episodes. An entity that is "planet" tier in one season may become "anchor" in the megagraph because they recur across multiple seasons.
• —Season-unique entities preserved: Entities appearing in only one season are transferred with their original UUIDs and properties.
• —Cross-season relationship topology: The megagraph contains participation and narrative connection patterns that span season boundaries.

For single-season analysis, use the individual season datasets:

• —Season 1
• —Season 2
• —Season 3
• —Season 4
• —Season 5
• —Season 6
• —Season 7

For cross-season analysis (character arcs, thematic evolution, entity importance across the full series), use this megagraph.

Files

Schema

Nodes (nodes.parquet)

Edges (edges.parquet)

All relationship properties are carried verbatim inside properties_json. Notably, PARTICIPATED_AS edges may carry incarnation_identifier (the extractor's free-text label for the identity the character appears under) and, where the persona normalisation pass has run, persona — a controlled value reused verbatim across episodes, absent when the character appears as themselves (schema v1.2.1).

Positions (positions.parquet)

Layout method (schema ≥ 1.2.0): Coordinates are derived from the entities' semantic text embeddings (UMAP with a fixed seed and PCA initialization), so narratively similar entities sit near each other. Non-embedded nodes (events, scenes, episodes, etc.) are placed at the weighted barycenter of their narrative neighbours. The layout is deterministic: re-exporting an unchanged graph reproduces identical coordinates, and lightly-changed graphs keep comparable layouts. Not comparable with positions published under schema ≤ 1.1.0, which used a non-deterministic node2vec structural embedding. See meta.json → positions for the exact method and coverage stats.

Usage

from datasets import load_dataset
import pandas as pd

# Load from HuggingFace
ds = load_dataset("brandburner/startrektng-mega-narrative-kg")

# Or load parquet directly
nodes = pd.read_parquet("nodes.parquet")
edges = pd.read_parquet("edges.parquet")

# Filter to anchor characters
anchors = nodes[(nodes['primary_label'] == 'Agent') & (nodes['tier'] == 'anchor')]

# Build a NetworkX graph
import networkx as nx
G = nx.DiGraph()
for _, n in nodes.iterrows():
    G.add_node(n['node_id'], label=n['primary_label'], name=n['name'])
for _, e in edges.iterrows():
    G.add_edge(e['source_node_id'], e['target_node_id'], type=e['relationship_type'])

Citation

@misc{fabula_startrektng_mega,
  title = {Star Trek: The Next Generation Narrative Knowledge Graph},
  author = {Fabula Pipeline},
  year = {2026},
  publisher = {HuggingFace},
  howpublished = {\url{https://huggingface.co/datasets/brandburner/startrektng-mega-narrative-kg}}
}

License

CC BY-SA 4.0