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

Metric	Value
Source database	startrektng.mega
Type	Megagraph (cross-season merged)
Episodes	177
Seasons merged	1, 2, 3, 4, 5, 6, 7
Total nodes	50,646
Total edges	134,915
Schema version	1.1.0
Exported	2026-03-14

Entity Breakdown

Type	Count
Act	935
Agent	2,102
ConflictArc	588
Episode	177
Event	9,333
Location	2,464
Object	8,364
Organization	790
PlotBeat	20,286
SceneBoundary	5,378
Theme	95
Writer	134

Graph Gravity Tiers

Tier	Count	Description
anchor	306	Main characters / key locations
planet	860	Recurring entities
asteroid	12,554	Minor / one-off entities

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 7 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

File	Description
nodes.parquet	All graph nodes with properties
edges.parquet	All relationships with properties
positions.parquet	3D layout coordinates for visualization
meta.json	Dataset metadata and entity counts

Schema

Nodes (nodes.parquet)

Column	Type	Description
node_id	string	Unique node identifier (UUID)
primary_label	string	Node type (Agent, Location, Event, etc.)
name	string	Display name
description	string	Foundational description
tier	string (nullable)	Graph Gravity tier: anchor / planet / asteroid
episode_count	int (nullable)	Number of distinct episodes entity appears in
first_episode_seq	int (nullable)	First appearance episode
last_episode_seq	int (nullable)	Last appearance episode
properties_json	string	Full node properties as JSON

Edges (edges.parquet)

Column	Type	Description
source_node_id	string	Source node UUID
target_node_id	string	Target node UUID
relationship_type	string	Relationship type (e.g., PARTICIPATED_AS)
properties_json	string	Edge properties as JSON

Positions (positions.parquet)

Column	Type	Description
node_id	string	Node UUID
x, y, z	float	3D coordinates
size	float	Node size (Graph Gravity weighted)
r, g, b	int	RGB color by entity type
community	int	Louvain community index
tier	string (nullable)	Graph Gravity tier

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