brandburner/doctorwho-s01-narrative-kg

Doctor Who - Narrative Knowledge Graph

A rich narrative knowledge graph extracted from Doctor Who 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	doctorwho.s01
Type	Season database
Episodes	42
Total nodes	4,597
Total edges	19,938
Schema version	1.2.0
Exported	2026-08-05

Entity Breakdown

Type	Count
Act	121
Agent	206
ConflictArc	83
Episode	42
Event	815
Location	359
Object	696
Organization	93
PlotBeat	1,490
SceneBoundary	508
Theme	178
Writer	6

Graph Gravity Tiers

Tier	Count	Description
anchor	33	Main characters / key locations
planet	196	Recurring entities
asteroid	1,125	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, EXEMPLIFIES_THEME, FORESHADOWING, INVOLVED_IN_ARC, INVOLVED_WITH, IN_EVENT, NARRATIVELY_FOLLOWS, OCCURS_IN, PARTICIPATED_AS, PART_OF ... and 7 more

Related Datasets

This is a single-season dataset containing entities and events as extracted from Season 1 screenplays.

• Megagraph (all seasons unified): brandburner/doctorwho-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 (seeded, deterministic)
tier	string (nullable)	Graph Gravity tier

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/doctorwho-s01-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_doctorwho_s01,
  title = {Doctor Who Narrative Knowledge Graph},
  author = {Fabula Pipeline},
  year = {2026},
  publisher = {HuggingFace},
  howpublished = {\url{https://huggingface.co/datasets/brandburner/doctorwho-s01-narrative-kg}}
}

License

CC BY-SA 4.0