fix(docs): fix formatting issues in README.md
- adapt meta-/code-/obs- rules to the local Python benchmark pipeline - replace db-sql-ddl, code-config-env-scope, test-e2e-pytest with pipeline equivalents - add code-python-style, data-determinism, data-naming-units, bench-methodology, build-pipeline-tasks - normalize specs and README typography to ASCII per code-data-formatting - rewrite root CLAUDE.md trigger table; add .cursorrules and .gitignore - specs: pipeline plan and task specs 00-11 - rules: 19 binding rule files adapted for this project - docs: CLAUDE.md rule-trigger table - config: .cursorrules commit convention, .gitignore excluding out/ and .venv/ - docs: rewrite README.md with pipeline diagrams, setup guide, result placeholders - config: requirements.txt for the closed dependency list - datagen: make_lab_config.py writes out/config/lab_config.yaml and .done marker
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# Task 14 — Export: RDF (Metadata & Provenance Graph)
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## Goal
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RDF surface for the metadata/provenance layer only — never the numeric series (per ADR).
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## Input
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- `canonical/` snapshot headers + hierarchy, `schema/`, lineage.
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## Actions
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1. Mint stable IRIs for every data note, batch, coupon, sample, measurement (pattern `https://tribology.ufl.example/id/<entity>/<key>`; base IRI in config).
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2. Write `scripts/export_rdf.py` → `exports/rdf/metadata.ttl` (Turtle): hierarchy, measurement headers, atmosphere, instrument, source-file provenance. Vocabularies: PROV-O (provenance), QUDT (units), Dublin Core (citation), DCAT (dataset catalog), local `lab:` terms.
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3. Emit `exports/rdf/mappings.r2rml.ttl` — R2RML mappings from the PostgreSQL schema, ready for Ontop virtual deployment.
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4. Numeric series: reference-only — each measurement IRI carries a `lab:dataService` pointer, no point triples.
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## Intermediate Result (done when)
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- `metadata.ttl` parses (validate with `rdflib`); triple counts reported per class.
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- R2RML mappings cover all metadata tables. `EXPORT_MANIFEST.json` written.
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