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Datasets:
Somnia
/
depesche-wd-index

Languages:
English
Spanish
Russian
Size:
10M<n<100M
Tags:
wikidata
knowledge-graph
entity-linking
entity-resolution
vector-search
knowledge-base
License:
Dataset card Files
xet
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The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
format_version: int64
built_at_unix: int64
source_dump: string
entity_count: int64
item_count: int64
property_count: int64
resolve_langs: list<item: string>
  child 0, item: string
display_lang_fallback: list<item: string>
  child 0, item: string
build_min_sitelinks: int64
notable_entity_count: int64
skipped_non_notable: int64
hot_props: list<item: int64>
  child 0, item: int64
facet_roots: list<item: struct<name: string, qid: int64>>
  child 0, item: struct<name: string, qid: int64>
      child 0, name: string
      child 1, qid: int64
limit: null
display_lang_non_en_sitelinks_ge_5: int64
display_lang_last_resort_sitelinks_ge_5: int64
walk: struct<stop_nodes: list<item: int64>, max_depth: int64>
  child 0, stop_nodes: list<item: int64>
      child 0, item: int64
  child 1, max_depth: int64
notable_universe: int64
facets: list<item: struct<name: string, root_qid: int64, members: int64, notable_universe: int64, coverage:  (... 8 chars omitted)
  child 0, item: struct<name: string, root_qid: int64, members: int64, notable_universe: int64, coverage: double>
      child 0, name: string
      child 1, root_qid: int64
      child 2, members: int64
      child 3, notable_universe: int64
      child 4, coverage: double
to
{'notable_universe': Value('int64'), 'walk': {'stop_nodes': List(Value('int64')), 'max_depth': Value('int64')}, 'facets': List({'name': Value('string'), 'root_qid': Value('int64'), 'members': Value('int64'), 'notable_universe': Value('int64'), 'coverage': Value('float64')})}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 149, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 129, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 489, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2818, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              format_version: int64
              built_at_unix: int64
              source_dump: string
              entity_count: int64
              item_count: int64
              property_count: int64
              resolve_langs: list<item: string>
                child 0, item: string
              display_lang_fallback: list<item: string>
                child 0, item: string
              build_min_sitelinks: int64
              notable_entity_count: int64
              skipped_non_notable: int64
              hot_props: list<item: int64>
                child 0, item: int64
              facet_roots: list<item: struct<name: string, qid: int64>>
                child 0, item: struct<name: string, qid: int64>
                    child 0, name: string
                    child 1, qid: int64
              limit: null
              display_lang_non_en_sitelinks_ge_5: int64
              display_lang_last_resort_sitelinks_ge_5: int64
              walk: struct<stop_nodes: list<item: int64>, max_depth: int64>
                child 0, stop_nodes: list<item: int64>
                    child 0, item: int64
                child 1, max_depth: int64
              notable_universe: int64
              facets: list<item: struct<name: string, root_qid: int64, members: int64, notable_universe: int64, coverage:  (... 8 chars omitted)
                child 0, item: struct<name: string, root_qid: int64, members: int64, notable_universe: int64, coverage: double>
                    child 0, name: string
                    child 1, root_qid: int64
                    child 2, members: int64
                    child 3, notable_universe: int64
                    child 4, coverage: double
              to
              {'notable_universe': Value('int64'), 'walk': {'stop_nodes': List(Value('int64')), 'max_depth': Value('int64')}, 'facets': List({'name': Value('string'), 'root_qid': Value('int64'), 'members': Value('int64'), 'notable_universe': Value('int64'), 'coverage': Value('float64')})}
              because column names don't match

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Depesche Wikidata Hot Index

A prebuilt, file-backed Wikidata index optimized for entity linking (resolve), facet / type checks (is_a), compositional queries, and multilingual semantic similarity — no external database required. Everything is mmap-friendly on-disk artifacts (FST, roaring bitmaps, CSR graphs, usearch vector shards).

Reader / builder code: https://github.com/mrink68/depesche-wd-index (Rust crates + wd CLI + PyO3 bindings)

Provenance

  • Source: official Wikidata JSON dump latest-all.json.gz (~155 GB compressed), snapshot from July 2026
  • Build floor: build_min_sitelinks = 2 → 14,213,939 notable items kept out of 120,905,360 scanned entities (P279 class edges are collected for all entities)
  • Index format: format_version 4
  • Resolve languages: 34 (aliases ingested for en es ru fa ko ar zh ja de fr pt it tr id vi th hi uk pl nl sv he ms bn ur el cs ro hu fi no nb nn ca)
  • License: Wikidata content is CC0 1.0; this dataset is a derived transformation of it.

Contents (~30 GB)

File Size What it is
meta.json 3 KB Build metadata: counts, languages, facet roots, hot properties
entity_hot.bin 512 MB Fixed-width hot rows (36 B/entity) incl. sitelinks_n
entity_warm.zst 6.4 GB Per-entity frames: display label + thin claims/sitelinks (zstd, warm.zdict dictionary)
entity_dir.bin 341 MB QID → hot/warm offset directory
aliases.fst 2.3 GB lang\0normalized_surface → postings offset (resolve index)
alias_postings.bin 2.0 GB Per-surface (qid u32, sitelinks_n u16) postings, popularity DESC
edges.csr 1.4 GB Undirected item-claim graph for path() BFS
p279_parents.csr / p279_children.csr 57 / 23 MB Subclass-of closure graphs
p31_edges.bin / p279_edges.bin / p131_edges.bin 119 / 42 / 27 MB Raw typed edges
postings/P*.bin 95 MB Filterable postings for P31, P17, P27, P39, P102, P106, P131, P279 (match/inbound)
facets/*.roaring + summary.json 20 MB Precomputed type bitsets (human, city, business, airport, film, …, 28 roots)
coords.bin 46 MB Geohash-bucketed P625 coordinates for near()
vectors.f16.matrix 10.9 GB 14,213,256 × 384 fp16 embedding matrix
vectors.shard00..02.usearch 3 × 2.5 GB Sharded ANN indexes (i8, cosine) — sharded because usearch breaks past 4 GiB/index
vectors.shards.json / vectors.meta.json — Shard map + embedding provenance
warm.zdict 110 KB zstd dictionary for entity_warm.zst

Embeddings: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2, mean pooling, L2-normalized, text template "{label}. {description}".

Two build intermediates are intentionally not included because they are regenerable from the files above using the repo code:

  • aliases.tsv (11.5 GB) — rebuild from warm frames via wd materialize (rebuild_aliases_tsv_from_warm), or skip it: resolve uses aliases.fst directly
  • embed_corpus.tsv (0.9 GB) — regenerate with wd export-embed-corpus (~18 min)

Usage

Download the dataset into an index/ directory, then query it with the code from the GitHub repo:

hf download Somnia/depesche-wd-index --repo-type dataset --local-dir ./index

git clone https://github.com/mrink68/depesche-wd-index
cd depesche-wd-index && cargo build -p wd-cli --release

./target/release/wd resolve --dir ../index "Spain" --lang en --limit 10
./target/release/wd is-a   --dir ../index Q31 sovereign_state
./target/release/wd doctor --dir ../index   # verify every artifact

Or from Python (PyO3 bindings, crates/wd-py):

from wd_index import Index
idx = Index("./index")

idx.resolve("7-Eleven", lang="th")                        # alias FST, popularity-ranked
idx.match_([("P106", "Q82955"), ("P27", "Q183")])         # German politicians
idx.value_at("Q22686", "P39", 2019)                        # position held in 2019
idx.near(48.1374, 11.5755, radius_km=30, facet="airport")  # geo query
idx.similar("Q90", k=20)                                   # multilingual ANN over MiniLM vectors

Full query surface (resolve / get / is_a / match / inbound / instances_of / types / subclasses / neighbors / value_at / top / count_by / near / path / similar) is documented in the repo README.

Notes

  • Artifacts are little-endian; build and query on the same endianness (x86_64 is fine).
  • Treat the directory as immutable — replace atomically on rebuild.
  • Run wd doctor after download: it verifies every artifact and names what each absence silently breaks.
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Total file size:
32 GB