brandburner/startrektng-s04-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.s04
Type	Season database
Episodes	26
Total nodes	7,936
Total edges	25,226
Schema version	1.1.0
Exported	2026-03-14

Entity Breakdown

Type	Count
Act	132
Agent	459
ConflictArc	104
Episode	26
Event	1,414
Location	456
Object	1,595
Organization	124
PlotBeat	2,713
SceneBoundary	881
Writer	32

Graph Gravity Tiers

Tier	Count	Description
anchor	45	Main characters / key locations
planet	170	Recurring entities
asteroid	2,419	Minor / one-off entities

Relationship Types

AFFILIATED_WITH, BELONGS_TO_EPISODE, CALLBACK, CAUSAL, CHARACTER_CONTINUITY, CONTAINS_ACT, CONTAINS_BEAT, CONTAINS_SCENE, CREDITED_ON, EMOTIONAL_ECHO, ESCALATION, FORESHADOWING, INVOLVED_IN_ARC, INVOLVED_WITH, IN_EVENT, NARRATIVELY_FOLLOWS, OCCURS_IN, PARTICIPATED_AS, PART_OF, PART_OF_ACT ... and 5 more

Related Datasets

This is a single-season dataset containing entities and events as extracted from Season 4 screenplays.

• Megagraph (all seasons unified): brandburner/startrektng-mega-narrative-kg

Note: The megagraph is not a simple union of season datasets. Cross-season entities are reconciled through a Global Entity Registry (GER), receiving new canonical UUIDs and distilled descriptions. Graph Gravity tiers are recalculated across all episodes. Use individual season datasets for single-season analysis; use the megagraph for cross-season analysis.

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-s04-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_s04,
  title = {Star Trek: The Next Generation Narrative Knowledge Graph},
  author = {Fabula Pipeline},
  year = {2026},
  publisher = {HuggingFace},
  howpublished = {\url{https://huggingface.co/datasets/brandburner/startrektng-s04-narrative-kg}}
}

License

CC BY-SA 4.0