A minted entity is a permanent, machine-readable provenance record — built to the standard we would want AI reading about us.
Every entity in the Boise Standard directory is minted. Minting is the act of crawling a live source, measuring every measurable dimension of it, assembling those measurements into a structured provenance record with a permanent identifier, and publishing that record at a stable URL where AI systems, researchers, and humans can read it — in that order, simultaneously, without ambiguity about what was measured, when, and by what method.
The record does not describe the entity. It measures it. The distinction is not semantic. A description carries the author's interpretation. A measurement carries the author's methodology, and the methodology is published alongside the output. Every number on a Boise Standard entity page traces to a specific pipeline stage, a specific crawl timestamp, a specific source URL. The chain of custody is unbroken from the raw HTTP response to the rendered page.
The living example of what a minted entity record looks like — in full — is boisestandard.org/web/hamstrahvac-com. That page is the reference implementation. Everything described on this page is present on that page, in the code, right now.
The Boise Standard refinery pipeline crawls the source domain, extracts every schema.org block from every interior page it can reach, measures the full text corpus for structural topology, scores the schema implementation against the declared type's property neighborhood, generates a machine-readable atomic answer grounded in the measured fields, and assembles all of it into a Root-LD traveling context pod embedded directly in the page head.
An AI crawler hitting a Boise Standard entity profile gets complete provenance on the first HTTP request. No body parse required. The structured data in the head carries the entity's full measurement record — identity, schema graph, topology fingerprint, semantic signal, gap analysis, atomic answer, and a recursive layer initialized at mint and ready to receive corpus edges as the graph grows.
This is not a directory listing. It is a provenance record built to the standard the machine-readable web requires and the community deserves.
Six stages. Every measurement deterministic.
Same input produces the same output. Always.
The pipeline is not a black box. Every stage is named, timed, and stamped in the record it produces. The hamstrahvac.com entity was minted in 26.33 seconds across six stages. Every entity in the directory was minted by the same pipeline, in the same sequence, against the same vocabulary.
.atomic-answer-text in the page head. Stage 5 timing for hamstrahvac.com: 2.214 seconds.A schema gap is not a technical deficiency.
It is a question AI cannot answer about you.
Schema.org is the shared vocabulary of the machine-readable web. Founded in 2011 by Google, Microsoft, Yahoo, and Yandex, it is the standard by which web publishers declare structured facts about themselves — their identity, their services, their location, their ratings, their credentials — in a format any machine can read without inference.
The Web Almanac 2024 found that only 44 percent of domains carry any schema markup at all, and of those, coverage of the available property space is typically shallow. The majority of local business pages implement a narrow slice of what schema.org makes available — address, name, phone number — and leave the rest undeclared. Web Almanac 2024 — Structured Data chapter.
The Boise Standard GDR Weighted Coverage Score measures how completely a given entity has implemented the schema.org property space available to its declared type. A score of 37% — like hamstrahvac.com at the time of minting — means 69 recommended properties are not yet implemented. Each of those 69 gaps is a specific question AI cannot answer accurately about that entity from structured data alone. It must infer. Inference introduces error. Error at scale becomes hallucination.
The score is not a grade. It is a measurement of the gap between what an entity has declared about itself and what the schema.org vocabulary makes available for that type of entity. Verification closes that gap — permanently, in the record, in the language AI reads.
Research on schema.org implementation in AI retrieval contexts confirms that business identity types — LocalBusiness, Organization, FAQPage — produce the strongest signal for AI citation. A 2024 study by Stackmatix found that entities implementing Tier 1 schema types saw a 3-to-1 improvement in AI citation rate over unstructured pages. Stackmatix — Structured Data and AI Search. The gap list on every Boise Standard entity page is a prioritized, specific, actionable list of what would close that gap for that entity.
Schema.org v30.0 was published March 2026. The Boise Standard refinery runs against the latest published vocabulary. Every entity record carries the vocabulary version and analysis timestamp. schema.org/version/latest.
Three layers. Every entity. Every page.
An AI crawler gets complete provenance on the first request.
Root-LD is a linked data specification developed for the Boise Standard project from independent research into frontier AI systems, knowledge graph architecture, provenance standards, and information theory. The W3C JSON-LD 1.1 specification — the underlying format — is published at w3.org/TR/json-ld11. The Root-LD specification is published at root-ld.org. Boise Standard is the first deployment at regional scale.
The anchor layer is sealed at the moment of minting and never modified. It contains the UUID, Federation ID, content hash of the extracted text, primary source URL, source verification flag, generation method, pipeline version, queued timestamp, mint timestamp, sequence number, domain signature, and the full manifest — a complete inventory of what the record contains and where each section lives.
The manifest includes a table of contents with permanent URLs pointing to every sub-record: body, schema graph, topology, semantic keywords, atomic answer, manifest JSON, Root-LD JSON, and the recursive edge collections. The link pod contains direct URLs to the canonical source, the TLD graph edge, the official schema.org vocabulary, and the Boise Standard vocabulary.
An AI system reading the anchor layer knows exactly what it is looking at, when it was created, by what method, from what source, and where every piece of the record lives. Full chain of custody from the first request.
The body layer is a complete measurement snapshot of the entity at the moment of minting. It is frozen — a new mint produces a new body, but this body does not change. It contains nine named subsections: identity, SEO, schema, semantic, topology fingerprint, ratio signals, navigation, provenance, graph edges, pipeline timing, and atomic answer.
The topology fingerprint is a six-layer pre-linguistic shape measurement of the full extracted text corpus — 12 deterministic values including type-token ratio, hapax ratio, repetition score, sentence skewness, kurtosis, punctuation entropy, and capital token ratio — sealed with a SHA-256 hash of the extracted text. The semantic section contains the top 40 words by frequency after stop-word removal, with no language classification, no dictionary matching, no editorial layer. Pure signal from what the entity chose to say about itself.
The ratio signals are eight deterministic measurements: schema density, nav ratio, content-to-structure ratio, external TLD diversity, self-declaration coherence, schema-to-navigation alignment, JavaScript surface ratio, and URL depth distribution. Each traces to a specific pipeline stage.
The atomic answer — a machine-generated summary grounded in the measured fields — carries the model identifier and a SHA-256 hash of the input. It is the most important field in the body for AI retrieval: it is what gets read first, and it is grounded in measurement, not inference.
The recursive layer is initialized at mint with zero edges, an empty edge list, and an empty append timestamp list. This is correct. It is not a deficiency. The recursive layer is the future tense of every entity record — the layer that accumulates connections between entities as the corpus grows deep enough to make those connections meaningful.
Common edges connect entities that share schema type neighborhoods — what two HVACBusiness entities share that no Restaurant shares. Uncommon edges connect entities across structural boundaries — the signal that appears in one topology cluster and not another. Jurisdictional edges connect entities to the geographic and regulatory structures they operate within. Supply chain edges connect entities through the product and service relationships they declare.
The graph builds itself. No editor decides which entities are related. The corpus passes over the accumulated records and the edges emerge from the measurements. This is Constitutional Law VII — Torus. The record reads the records.
The research foundation for the Root-LD architecture draws from multiple independent disciplines. Knowledge graph construction for AI retrieval: Neo4j — Unstructured Text to Knowledge Graph. Knowledge graph accuracy improvement in AI systems: WordLift — 29.8% accuracy improvement with knowledge graph enrichment. LLM-driven knowledge graph construction at scale: NVIDIA — LLM-driven Knowledge Graph techniques.
Data provenance and its role in AI accuracy: Zyte — What Is AI Data Provenance. Data quality and hallucination prevention: DataScienceCentral — Data Quality for Unbiased AI Results. IBM on AI data quality: IBM — AI and Data Quality.
Schema.org structured data and AI search: Google — Introduction to Structured Data. Entity linking and disambiguation at scale: SchemaApp — Entity Linking for Disambiguation. The @id and @graph pattern for knowledge graph construction: Momentic — @id Schema for SEO, LLMs, and Knowledge Graphs.
The shift from search to answer engine retrieval: SimilarWeb — Answer Engine Optimization. AI session growth: Frase — AI sessions up 527% year over year in 2025. AI Overviews reducing click-through rate: Jasper — AI Overviews reduced CTR 58%.
Seven laws govern every decision the pipeline makes.
They are in the code. They are in the output. They are non-negotiable.
The Constitutional Laws of Information are not a philosophy statement. They are implemented constraints — rules that govern every pipeline decision, every output field, every display choice on every entity page. When you see a label on a Boise Standard entity page that says "Law I — Provenance" or "Law III — meaning is yours," that label is not decorative. It is the specific law that governs what is shown and why it is shown that way.
The pipeline measures what is findable.
Verification declares what is true.
Every unverified entity record on Boise Standard is built from what the pipeline could find on the open web. Verification is the act of the entity itself claiming the record and filling in what the pipeline cannot find — the founding story, the legal name, the credentials, the corrections to what AI currently gets wrong, and the declaration of who this entity actually is in its own words.
The unverified fields panel on every entity page lists every slot the pipeline could not fill from the open web. The hamstrahvac.com entity record, minted June 13, 2026, had 30 named empty slots across eight categories — entity identity, location and service area, what the entity does, credentials and trust, voice and authority, ratings and digital presence, media and documents, and final notes.
The most powerful slot on the panel is this one: What AI Currently Gets Wrong About You. Corrections go directly into the entity record as boundary declarations. An entity that has been mischaracterized — by a hallucinating model, by an outdated training set, by a competitor's SEO strategy — can declare the correction in its own words, in the permanent record, in the exact format AI reads. No intermediary. No platform dependency. The record belongs to the entity.
Verification also produces the complete JSON-LD schema file — built from the verification questionnaire, delivered via email with head placement instructions, ready to implement on the entity's own website. The schema file closes the gap list. Every property named in the gap list becomes a declared property. The schema coverage score rises. The questions AI could not answer become questions AI can answer — accurately, from the verified record.
The verification deliverables for every $25 verification: verified entity profile, complete JSON-LD schema file, full site analysis report, AI optimization recommendations, AI readiness guide, sitemap submission walkthrough, and Certificate of Verified Provenance — Certificate ID format BS-2026-000001, issued by Boise Standard LLC.
The machine-readable web for the Treasure Valley
is being built one minted entity at a time.
The Web Almanac 2024 documented that 44% of domains carry any structured data, and only 12.4% of all registered domains carry schema markup of any kind. The majority of businesses operating in the Treasure Valley today are invisible to the machine-readable web — not because they have nothing to say, but because no one has built the infrastructure to say it for them and with them, in a format machines can read and communities can own.
Boise Standard is that infrastructure. Every minted entity in the directory is a permanent, machine-readable, provenance-sealed record of a real entity in this region — measured by a reproducible pipeline, grounded in open standards, governed by seven Constitutional Laws, and available to any AI system, researcher, or person who needs it.
The verified record of the Treasure Valley belongs to the Treasure Valley. Not to a platform. Not to a model. Not to an algorithm that changes without notice. The record is here. The standard is set. The graph builds itself.