---
license:
  - cc-by-sa-4.0
  - cc0-1.0
language:
  - en
pretty_name: Ballast T1
size_categories:
  - 10M<n<100M
task_categories:
  - text-retrieval
tags:
  - knowledge-base
  - wikipedia
  - rag
  - grounding
  - hallucination
configs:
  - config_name: passages
    data_files: "passages/lic=by-sa/rank_bucket=*/*.parquet"
  - config_name: statements
    data_files: "statements/statements.parquet"
  - config_name: labels
    data_files: "statements/labels.parquet"
---

# ⚓ Ballast T1

The prose tier of the Ballast knowledge artifact: **full-body English
Wikipedia as rank-quantized passage chunks**. It spans 37.4M chunks across
7.0M articles (11.9 GB zstd parquet), in the same nested-bucket layout as
[Ballast T0](https://huggingface.co/datasets/OpenBallast/ballast-t0). Where
T0 carries facts as triples, T1 carries the paragraphs those facts live in.
This is the tier for narrative and long-tail questions that triples cannot
serve.

![Chunks per bucket and cumulative download size](assets/t1_buckets.png)

**Measured context** (all numbers, protocol, and caveats:
[results-retrieval.md](https://github.com/OpenBallast/ballast/blob/main/docs/results-retrieval.md)):
with **Qwen3.5-9B-Base (int8)** answering open-ended (≤24 tokens, normalized
alias-containment grading) on 2,000 probes from **SimpleQA + NQ-open**,
grounding against this corpus with a CPU-only retriever moves accuracy
**0.1540 → 0.3315** (single-pass 0.2885; the two-pass support-check
architecture adds the rest, and revalidated held-out on PopQA). At equal or
less GPU memory, that beats a 12B model running alone (0.1565) by
**+17.5 points**. These are generative string-match numbers: a different
and harder instrument than the logprob multiple-choice scores on the T0
card; the two do not compare.

## Layout

Hive-partitioned Parquet; the partition key is the corpus-quantization axis
(same rank composite as T0: log sitelinks + log claim count). A level Lk =
buckets 0..k. `manifest.json` at the repo root carries per-file counts,
bytes, sha256 hashes, and build provenance.

```
passages/lic=by-sa/rank_bucket={0..7}/*.parquet
statements/statements.parquet     # 31.9M whitelisted Wikidata statements (CC0)
statements/labels.parquet         # 10.2M entity labels for rendering them
manifest.json
```

`passages` schema: `qid`, `chunk_idx`, `title`, `heading`, `text`
(+ `rank_bucket` and `lic` from the path). Chunks are paragraph runs merged
greedily to a ≈1.2 KB target and are **atomic**: serving stacks pack whole
chunks or skip them, never truncate. This is the byte-identity condition the
published measurements depend on.

| bucket | chunks | articles | bytes | cumulative |
|---|---|---|---|---|
| 0 | 1.96M | 75K | 662 MB | 0.66 GB |
| 1 | 0.76M | 76K | 258 MB | 0.92 GB |
| 2 | 1.46M | 150K | 582 MB | 1.50 GB |
| 3 | 2.25M | 332K | 784 MB | 2.29 GB |
| 4 | 3.28M | 642K | 1.00 GB | 3.29 GB |
| 5 | 4.69M | 931K | 1.40 GB | 4.69 GB |
| 6 | 7.04M | 1.34M | 2.13 GB | 6.82 GB |
| 7 | 16.0M | 3.47M | 5.05 GB | 11.86 GB |

## Use it

```sql
SELECT chunk_idx, heading, text
FROM read_parquet('ballast-t1/passages/**/*.parquet', hive_partitioning=true)
WHERE rank_bucket <= 3 AND qid = 'Q42'
ORDER BY chunk_idx;
```

To download only a level:
`snapshot_download("OpenBallast/ballast-t1", repo_type="dataset",
allow_patterns=["passages/*/rank_bucket=[0-3]/*", "manifest.json"])`.

## Run it locally (serving format)

`serving/sqlite/` carries the same chunks (byte-identical to the parquet
distribution) as per-bucket SQLite for the
[`ballast` CLI](https://github.com/OpenBallast/ballast-cli) (v0.3.0+).
The name index joins T0 labels and aliases by QID, with a
normalized-title fallback for the 1.28M tail articles T0 never named.
Entity-name lookup over prose is functional but unbenchmarked: the
measured retrieval numbers above come from the two-pass research stack,
which the CLI does not run yet.

```bash
uvx openballast pull --tier t1 --level 3   # 1.6 GB download, 8.7 GB on disk
uvx openballast serve --corpus t1          # proxy :11435 + MCP :11436
```

| level | download | on disk | | level | download | on disk |
|---|---|---|---|---|---|---|
| L0 | 0.52 GB | 2.0 GB | | L4 | 2.42 GB | 12.1 GB |
| L1 | 0.71 GB | 3.1 GB | | L5 | 3.52 GB | 16.7 GB |
| L2 | 1.09 GB | 5.7 GB | | L6 | 5.16 GB | 23.5 GB |
| L3 | 1.64 GB | 8.7 GB | | L7 | 8.98 GB | 40.5 GB |

Sizes are measured from `serving/sqlite/serving_manifest.json` (per-file
bytes + sha256), which `pull` also reads to print cost before
downloading.

The `statements` config is the typed-fact sidecar (subject `s`, property
`prop`, object `o` or literal `o_lit`), restricted to a ~70-property
whitelist over English-Wikipedia entities, useful as a structured
cross-check on prose answers.

## Curation & processing

Not a Wikipedia mirror: a chunked, rank-quantized, license-cellular
derivation of one. The pipeline, in order:

1. **Scope.** Article namespace only, from the `pages-articles-multistream`
   dump; redirects resolved (their titles feed the alias index rather than
   producing duplicate articles); Category/Portal/Template pages excluded.
   The build covers 99.4% of the dump's 7.54M articles.
2. **Section filtering.** References, external links, see-also,
   bibliographies, galleries and similar tail sections dropped by heading
   match before parsing.
3. **Stripping & chunking** (`t1-chunk-v1`). Wikitext stripped with a
   deliberately simple parser: templates, infoboxes, and tables are
   *dropped, not rendered* (cost measured and stated under Limitations);
   paragraphs merged greedily to a ≈1.2 KB atomic-chunk target, first
   chunk of each section prefixed `Title — Heading:` for self-containment.
4. **Rank join.** Articles joined to the T0 entity rank map by QID, so the
   prose tier shares the triple tier's notability axis and one entity
   spine; unmapped articles land in the tail bucket rather than being
   dropped. This cross-tier alignment (same QIDs, same buckets, prose +
   triples + statements composable) distinguishes this from existing
   passage dumps (KILT, wiki-dpr and kin are also years staler and
   single-granularity).
5. **Dedup.** Exact (qid, chunk_idx) duplicates removed across parse
   shards at packaging.
6. **Statements sidecar.** The CC0 statements config is Wikidata truthy
   statements restricted to a ≈70-property whitelist (media, sports,
   family, positions, taxa: the relation types the eval populations
   actually ask about), both endpoints restricted to English-Wikipedia
   entities, plus the labels needed to render them.

## Limitations

- **Raw encyclopedia prose, shipped verbatim.** Chunks contain whatever
  English Wikipedia contained at the snapshot: biographical detail about
  living people, descriptions of violence and other sensitive topics, and
  any vandalism that survived patrol. Filter downstream to your use case.
- **The parser is deliberately simple.** Templates, tables, and most markup
  are dropped; occasional markup residue (HTML comments, entities) survives
  in chunk text. A raw-vs-stripped census priced the cost at **3.2
  percentage points (NQ-open) / 4.4 (SimpleQA) of gold-answer capture**
  versus a perfect parser. The gap is accepted deliberately; the residue is
  noise, not silent loss.
- **Coverage follows notability**, as in T0: head buckets skew to globally
  notable topics; articles without a mapped T0 entity land in the tail
  bucket (7).
- **Staleness horizon**: the `enwiki-20260701` dump. Rebuilds are versioned
  releases.

## Provenance & license

Parsed from the English Wikipedia `pages-articles-multistream` dump
(`enwiki-20260701`): the same corpus build the published retrieval results
were measured against, plus the statements sidecar. Ranking joins the T0
rank map.

**Passages: CC BY-SA 4.0** (Wikipedia contributors). Attribution resolves
through each chunk's `title` to the source article and its revision history;
derivatives of the text carry share-alike. **Statements + labels: CC0**
(Wikidata contributors). The front-matter lists both licenses; each config's
license is stated here and in `manifest.json`. Companion artifacts:
[ballast-t0](https://huggingface.co/datasets/OpenBallast/ballast-t0),
[ballast-evalsets](https://huggingface.co/datasets/OpenBallast/ballast-evalsets).

Questions and issues: [github.com/OpenBallast/ballast/issues](https://github.com/OpenBallast/ballast/issues).