README.md

---
license: cc0-1.0
language:
  - en
pretty_name: Ballast T0
size_categories:
  - 100M<n<1B
tags:
  - knowledge-base
  - wikidata
  - rag
  - grounding
  - knowledge-graph
  - hallucination
configs:
  - config_name: entities
    data_files: "entities/tier=T0/lang=en/rank_bucket=*/*.parquet"
  - config_name: triples
    data_files: "triples/tier=T0/lang=en/rank_bucket=*/*.parquet"
  - config_name: properties
    data_files: "properties.parquet"
---

# ⚓ Ballast T0

**We measured how much of a bigger model's factual advantage is just memorized
trivia, and whether you can buy that back with a file instead of with
parameters. You can, and it's 40–100× cheaper per byte.**

This is that file: the reference *ballast* — a versioned, rank-quantized knowledge artifact you pair
with any language model. 25.4M entities, 197.4M cleaned Wikidata triples, 1.51 GB
total, published in **8 nested rank buckets**: pick your knowledge level like you
pick a GGUF quant.

Measured effect (50,147 linked probes, logprob choice scoring):

| Gemma-4 | raw | + full ballast | hallucination |
|---|---|---|---|
| E2B | 0.608 | **0.868** | 0.242 → **0.074** |
| E4B | 0.662 | **0.910** | 0.197 → 0.042 |
| 12B | 0.683 | **0.910** | 0.207 → 0.049 |

| Qwen3.5 | raw | + full ballast | hallucination |
|---|---|---|---|
| 0.8B | 0.324 | **0.784** | 0.601 → **0.114** |
| 2B | 0.363 | 0.770 | 0.552 → 0.137 |
| 4B | 0.434 | **0.831** | 0.474 → **0.095** |
| 9B | 0.542 | 0.819 | 0.329 → 0.113 |

The pattern replicates across two unrelated families: raw floors spread,
ballasted ceilings compress — and the **ballasted Qwen 4B beats the ballasted
9B outright**. With perfect entity resolution, **+180 MB of ballast lifts
Gemma E2B past the 12B's raw factual accuracy**; with the shipped
non-generative linker (which realizes about two thirds of that ideal gain),
the crossing needs **~470 MB**. Either way the parameter route to the same
gain costs ~19 GB — a ~40× byte disadvantage.

Two more measured effects worth knowing:

- **Ballasted accuracy is a quantization-damage diagnostic.** 4-bit (nf4)
  leaves Gemma E2B and 12B nearly intact but collapses E4B's ballasted
  ceiling from 0.910 to 0.650 — when quantization breaks a model's ability
  to *read* evidence, no corpus buys it back, and the grounded score is what
  reveals it.
- **This fixes answerable questions; it does not teach abstention.** On 2-hop
  composition probes, grounding cuts hallucination 3–20×. On questions with
  no true answer (false premises, never-recorded facts), fabrication *rises*
  with corpus coverage — evidence reads as license to answer.

Full thesis, methodology, and caveats:
[github.com/OpenBallast/ballast](https://github.com/OpenBallast/ballast).

## Layout & quantization levels

Hive-partitioned Parquet; the partition key **is** the quantization axis. A level
Lk = buckets 0..k — download fewer bucket directories to hold a smaller corpus:

```
entities/tier=T0/lang=en/rank_bucket={0..7}/*.parquet
triples/tier=T0/lang=en/rank_bucket={0..7}/*.parquet
properties.parquet    # 13,704 property labels, shared by every level
manifest.json         # counts, sizes, hashes, build provenance
```

| level | cumulative | entities |
|---|---|---|
| L0 | 36 MB | top 0.5% |
| L1 | 62 MB | top 1% |
| L2 | 107 MB | top 2% |
| L3 | 179 MB | top 4% |
| L4 | 288 MB | top 8% |
| L5 | 466 MB | top 16% |
| L6 | 756 MB | top 32% |
| L7 | 1,507 MB | all 25.4M |

Ranking = composite of log(sitelinks) + log(claim count). Buckets nest: L3 is a
byte prefix of L7. Truncation degrades honestly — an entity outside the level has
no evidence; a triple whose *object* falls outside the level is dropped, never
rendered as a bare Q-id.

## Schemas

`entities`: `qid`, `label`, `description`, `aliases[]`, `sitelinks`, `n_claims` (+ `rank_bucket` from path)
`triples`: `qid`, `pid`, `value_type` (`entity|time|quantity|string|coord`), `value` (+ subject's `rank_bucket`)

Excluded at publish: external identifiers, media references, URLs — bytes that
ground nothing a language model can use.

## Use it

DuckDB (the layout is built for it):

```sql
SELECT t.qid, p.label AS prop, t.value
FROM read_parquet('ballast-t0/triples/**/*.parquet', hive_partitioning=true) t
JOIN read_parquet('ballast-t0/properties.parquet') p USING (pid)
WHERE t.rank_bucket <= 3 AND t.qid = 'Q42';
```

Evidence rendering (the exact format the published numbers were measured under):

```
Facts about Douglas Adams:
- place of birth: Cambridge
- occupation: science fiction writer
...
```

## Run it locally (serving format)

`serving/sqlite/` carries the same corpus as zstd-compressed per-bucket SQLite —
the format consumed by the [`ballast` CLI](https://github.com/OpenBallast/ballast-cli):

```bash
uvx openballast pull --level 3     # 265 MB download -> ~1 GB on disk
uvx openballast serve              # OpenAI grounding proxy :11435 + MCP :11436
```

| level | download | on disk | | level | download | on disk |
|---|---|---|---|---|---|---|
| L0 | 52 MB | 0.2 GB | | L4 | 427 MB | 1.6 GB |
| L1 | 92 MB | 0.35 GB | | L5 | 691 MB | 2.6 GB |
| L2 | 159 MB | 0.6 GB | | L6 | 1.1 GB | 4.2 GB |
| L3 | 265 MB | 1.0 GB | | L7 | 2.2 GB | 9.2 GB |

Each `bucket_k.sqlite` holds that bucket's `entities`, `names` (normalized
label/alias index), and `triples`; levels attach buckets 0..k, so upgrading a
level downloads only the new buckets. Truncation semantics identical to the
parquet artifact. Parquet remains the canonical/build format; the sqlite tree is
a derived serving artifact.

## Live demo endpoint

Levels L0–L5 are served free at **`https://mcp.openballast.org`** (MCP over
streamable HTTP + plain GET) — running entirely on a $0/month free-tier stack as
an existence proof. Demo-grade: no auth, no SLA. Reference:
[docs/mcp.md](https://github.com/OpenBallast/ballast/blob/main/docs/mcp.md).

```bash
curl "https://mcp.openballast.org/lookup?question=Where+was+Douglas+Adams+born%3F&level=5"
```

## Provenance & license

Built from the Wikidata JSON dump (2026-07 snapshot; parameters and hashes in
`manifest.json`). `mul` language code honored with English fallback;
Wikimedia-internal pages filtered by `instance of`. **Data: CC0** (Wikidata
contributors). Companion eval sets:
[OpenBallast/ballast-evalsets](https://huggingface.co/datasets/OpenBallast/ballast-evalsets).