fabsssss/ssao-space-instruct

ssao-space-instruct

Instruction data teaching a language model to write valid RDF Turtle in the Space Situational Awareness Ontology (SSAO) for real space objects, and to judge proposed catalogue-to-ontology alignments using instance evidence.

1,245 examples: 1,072 train, 74 validation, 99 test. Built by Tesseract Academy.

The construction principle

No example asserts anything a validator cannot check. Every Turtle target is generated from a real CelesTrak SATCAT record by applying a published, argued SATCAT-to-SSAO alignment, so the ontology terms are correct by derivation rather than by authorship. A fail-closed vocabulary gate refuses to emit the dataset at all if any ssao: term in any target is missing from the ontology's authoritative 554-term set.

That gate earned its place immediately: the first builder version invented ssao:hasOrbit and ssao:hasInclination, which SSAO does not define (the real names are has_Orbit, has_Orbital_Inclination). Shipping those would have taught fluent hallucination in a domain whose ontology cannot reject it, which is exactly the failure mode the accompanying study measures.

Task families

T3 is the unusual one. Positive rows come from a curated crosswalk; negative rows come from its asserted non-mappings plus mappings that were empirically refuted by counting counter-instances in the catalogue, with the witness counts quoted as the justification. For example, refusing kg:RegimeGEORegion = ssao:Geostationary_Orbit cites 1,007 of 1,734 geosynchronous-band residents whose inclination exceeds 5 degrees. We are not aware of another public dataset covering alignment judgement with extensional evidence.

Sources and provenance

• —CelesTrak SATCAT, snapshot 2026-07-29, pinned by sha256. 70,122 objects; T1 and T4 draw on a random sample. Data courtesy of T.S. Kelso.
• —Space Situational Awareness Ontology by Robert J. Rovetto, in the copy vendored by the NASA mission-viz project (351 classes, 150 properties).
• —The alignment and the witness counts come from neurosymbolic-space-kg, which contains the builder, the validator, the vocabulary gate and the measurements. Everything regenerates from source.

Format

Chat-formatted JSONL, one object per line, with a fixed system prompt:

{"messages": [
  {"role": "system", "content": "You are a space-domain knowledge engineer..."},
  {"role": "user", "content": "Express this catalogue record as SSAO Turtle...."},
  {"role": "assistant", "content": "@prefix ssao: <https://purl.org/space-ontology/> ...."}
]}

Known limitations

• —Property coverage is deliberately narrow: only the SSAO properties the catalogue can populate honestly (COSPAR number, country of origin, launch date, orbit, inclination, period, perigee, apogee). Most of SSAO's 150 properties are untouched.
• —Status codes are operator-reported and can be stale; T1 targets inherit that.
• —Derived orbit regimes rest on stated thresholds (2000 km for LEO, a 1400 to 1500 minute geosynchronous band, 5 degrees for geostationary candidacy). They are conventions, printed in the builder and adjustable; a sensitivity sweep of their influence is published in the repository.
• —The alignment judgements are a curated position, reviewable and arguable, not ground truth.
• —Roughly 72 percent of examples are one task family (T1), so a model trained on this alone will be strongest at record-to-Turtle.

License

CC BY 4.0. SATCAT data courtesy of CelesTrak; SSAO by Robert J. Rovetto. Please attribute both alongside this dataset.