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 Wikidata — labels / aliases / properties from either the Wikidata API or a local .json.bz2 dump.

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
`pile_count`	int \| null	frequency of the label in the Pile (if provided)
`n_triples`	int	number of `triples` where this entity is the subject
`n_tokens_pythia`	int \| null	tokens of `label` under the Pythia (GPT-NeoX) tokenizer
`n_tokens_llama`	int \| null	tokens of `label` under the Llama-3.x tokenizer

`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 (hyperlink anchor for cross-page mentions; matched label/alias for self-mentions)
`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[]` (0 = relation best; may differ per triple with per-triple selection)
`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). origin records which source(s) each entity came from.
entities / documents: text + link graph from hugging face wikimedia/structured-wikipedia (enwiki); aliases & properties from Wikidata (API or local dump); abstract/text from structured-wikipedia; disambiguation pages filtered out. Mentions: an entity in its own page is found by matching its label + aliases; mentions in other pages come from hyperlink anchors (exact surface form + offset) by default, which avoids false positives (e.g. matching "Paris" inside Notre-Dame de Paris).
triples: linearity relations; relation_id is the Wikidata P-id from a static mapping. template is the best prompt — either per relation (max partial-match in generation) or, with per-triple selection, the candidate that maximises the gold object's teacher-forced log-prob for that specific (subject, object).
links: ZELDA entity-linking documents; mention wikipedia_id matched to entities.page_id → QID.

Code: https://github.com/siemovit/platovec

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