metadata
dataset_info:
- config_name: documents
features:
- name: document_id
dtype: string
- name: entity_ids
list: string
- name: aliases
list: string
- name: index
list:
list: int64
- name: entity_count
dtype: int64
splits:
- name: train
num_bytes: 5721
num_examples: 35
download_size: 20503
dataset_size: 5721
- config_name: entities
features:
- name: id
dtype: string
- name: label
dtype: string
- name: description
dtype: string
- name: alias
list: string
- name: wikidata_url
dtype: string
- name: wikipedia_url
dtype: string
- name: what_links_here
list: string
- name: properties
list: 'null'
- name: text
dtype: string
- name: abstract
dtype: string
- name: page_id
dtype: int64
- name: n_characters
dtype: int64
- name: pile_count
dtype: int64
splits:
- name: train
num_bytes: 97175
num_examples: 38
download_size: 235417
dataset_size: 97175
- config_name: links
features:
- name: source
dtype: string
- name: document_id
dtype: 'null'
- name: entity_ids
list: string
- name: index
list:
list: int64
- name: text
dtype: string
- name: wikipedia_ids
list: int64
- name: wikipedia_titles
list: string
splits:
- name: test
num_bytes: 104109
num_examples: 53
download_size: 74255
dataset_size: 104109
- config_name: spans
features:
- name: id
dtype: int64
- name: document_id
dtype: string
- name: entity_id
dtype: string
- name: alias
dtype: string
- name: boundaries
list:
list: int64
- name: wikipedia_url
dtype: string
splits:
- name: train
num_bytes: 13267
num_examples: 88
download_size: 11889
dataset_size: 13267
- config_name: templates
features:
- name: relation_id
dtype: string
- name: relation_name
dtype: string
- name: templates
list: string
splits:
- name: train
num_bytes: 4766
num_examples: 26
download_size: 12848
dataset_size: 4766
- config_name: triples
features:
- name: id
dtype: int64
- name: relation_id
dtype: string
- name: relation_name
dtype: string
- name: prompt
dtype: string
- name: subject
dtype: string
- name: subject_qid
dtype: string
- name: object
dtype: string
- name: object_qid
dtype: 'null'
splits:
- name: train
num_bytes: 1237660
num_examples: 9696
download_size: 482568
dataset_size: 1237660
configs:
- config_name: documents
data_files:
- split: train
path: documents/train-*
- config_name: entities
data_files:
- split: train
path: entities/train-*
- config_name: links
data_files:
- split: test
path: links/test-*
- config_name: spans
data_files:
- split: train
path: spans/train-*
- config_name: templates
data_files:
- split: train
path: templates/train-*
- config_name: triples
data_files:
- split: train
path: triples/train-*
platovec — Wikipedia entities, documents, linearity triples & ZELDA links
A dataset for studying entity representations in LLMs: how entities are encoded, how those representations form, and their causal effects.
Built from a seed entity list (the factual relations of Hernandez et al. 2024, by title, plus
ZELDA mention targets, by Wikipedia page id) by joining wikimedia/structured-wikipedia
(text + links) and the Wikidata API (labels / aliases / properties).
Configs
| config | granularity | description |
|---|---|---|
entities |
one row / entity | the entity, its text, abstract, aliases, properties, links |
documents |
one row / document | mention positions of entities within a document |
triples |
one row / linearity fact | (subject, object) + chosen template + QIDs + Wikidata property |
templates |
one row / relation | candidate prompt templates per relation (best-first) |
links |
one row / ZELDA document | entity-linking mentions for retrieval eval (split test) |
from datasets import load_dataset
entities = load_dataset("ykolo/entityconcepts", "entities", split="train")
documents = load_dataset("ykolo/entityconcepts", "documents", split="train")
triples = load_dataset("ykolo/entityconcepts", "triples", split="train")
links = load_dataset("ykolo/entityconcepts", "links", split="test")
entities
| column | type | description |
|---|---|---|
qid |
str | Wikidata QID (row key) |
label |
str | entity name (Wikipedia article title) |
description |
str | one-line description |
alias |
list[str] | alternative names (Wikidata "also known as", English) |
wikidata_url |
str | |
wikipedia_url |
str | |
what_links_here |
list[str] | QIDs of seed entities whose article links to this one |
properties |
list[{property_id, value}] | Wikidata claims (value = QID for items, rendered text otherwise) |
text |
str | flat article text (reconstructed from structured-wikipedia sections) |
abstract |
str | null | article lead/abstract (from structured-wikipedia) |
page_id |
int | null | English Wikipedia page id (structured-wikipedia identifier) |
n_characters |
int | null | article length (structured-wikipedia version.number_of_characters) |
origin |
list[str] | seed origin(s): linearity and/or zelda |
n_mentions |
int | total mention occurrences across the documents table |
documents
One row = one document (an entity's Wikipedia article). Lists are aligned by position:
entity_ids[i] ↔ aliases[i] ↔ index[i]. The document text is entities[document_id].text
(not duplicated here). Use it to extract an entity's activations at its mention positions
across several documents (self-mention ⇔ document_id ∈ entity_ids).
| column | type | description |
|---|---|---|
document_id |
str | QID of the entity whose article is this document (→ entities.qid) |
entity_ids |
list[str] | QID of each mentioned entity (repeats for multiple mentions) |
aliases |
list[str] | surface form found in the text for each mention |
index |
list[[int, int]] | character [start, end] offsets in the document's text |
entity_count |
int | number of distinct entities mentioned in the document |
triples
Facts from the factual linearity relations (one row per (subject, object) sample).
| column | type | description |
|---|---|---|
id |
int | |
relation_id |
str | null | mapped Wikidata property, e.g. P36 (null if unmapped) |
relation_name |
str | human-readable name, e.g. "country capital city" |
template |
str | prompt template with {} as the subject placeholder |
template_id |
int | null | index of the template used in templates.templates[] (best-first ⇒ 0) |
subject |
str | subject |
subject_qid |
str|null | qid of subject |
object |
str | object |
object_qid |
str|null | qid of object |
links
Entity-linking documents from the ZELDA dataset, for retrieval evaluation. One row = one
document; each mention's ZELDA wikipedia_id is matched to the page_id column of entities
to recover its QID. Split test (the source column gives the ZELDA subset, e.g. aida-b;
train is available too if built). Lists aligned by position:
entity_ids[i] ↔ index[i] ↔ wikipedia_ids[i] ↔ wikipedia_titles[i].
| column | type | description |
|---|---|---|
source |
str | split origin (train, or test set name e.g. aida-b) |
document_id |
str | null | QID of the document's own Wikipedia page (train only; via entities.page_id) |
entity_ids |
list[str] | QID of each mention's gold entity |
index |
list[[int, int]] | character [start, end] offsets in text |
text |
str | document text |
wikipedia_ids |
list[int] | raw ZELDA Wikipedia page ids of the mentions (traceability) |
wikipedia_titles |
list[str] | ZELDA Wikipedia titles of the mentions (traceability) |
Mentions whose wikipedia_id is not present in entities (entity not scraped) are dropped (logged).
Construction
- seed entities: subjects + objects of the factual relations (by title) + ZELDA mention
targets (by
page_id).originrecords which source(s) each entity came from. entities/documents: text + link graph from hugging facewikimedia/structured-wikipedia(enwiki); aliases & properties from the Wikidata API;abstract/textfrom structured-wikipedia; disambiguation pages filtered out;triples: linearity relations;relation_idis the Wikidata P-id from a static mapping;templateis the best prompt per relation (max partial-match in generation).links: ZELDA entity-linking documents; mentionwikipedia_idmatched toentities.page_id→ QID.