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AI consumer guide

This document is the consumption contract for Internacia Datasets — written for LLM agents, enrichment pipelines, and programmatic integrators. For installation and build instructions, see README.md.

Scope​

In scope​

Reference data used for entity linking, geographic classification, and organizational membership joins:

  • ISO 3166-1 identifiers and entity status metadata
  • Geography (borders, continents, subregions, coordinates, centroids where present)
  • Demographic reference fields (population, area, gini) with source/year
  • World Bank-style classifications (region, incomeLevel, lendingType)
  • Cultural reference (languages, currencies, timezones, demonyms, flag_emoji)
  • Multilingual names and aliases (other_names, common_names, native_names)
  • Wikidata entity links (wikidata_id)
  • Intergovernmental organizations with membership rosters and taxonomy
  • Field-level provenance on country records where enriched

Out of scope​

Do not infer or expect these fields — they are intentionally absent:

  • HDI, GDP, GDP per capita, government type, internet penetration
  • Time-series economic or governance indicators
  • Real-time membership or political recognition status

Downstream consumers should enrich from separate datasets. See openspec/AGENTS.md (Dataset Scope).

Datasets​

DatasetRecordsPrimary keyManifest
countries256code (alpha-2)data/datasets/countries.manifest.json
intblocks1272iddata/datasets/intblocks.manifest.json
blocktypes78iddata/datasets/blocktypes.manifest.json

All three are bundled in data/datasets/internacia.duckdb. Prefer DuckDB or Parquet over reading individual YAML source files under data/countries/ and data/intblocks/.

Consistency guarantee. CI (scripts/check_generated_artifacts.py) enforces that every export format (JSONL, YAML, Parquet, DuckDB) exposes the same primary-key set and row count per dataset, that these match the YAML source, and that all manifests, *.meta.json sidecars, and the DuckDB _meta table share one build identity (version, git_commit, build_date). You can rely on any format being complete and interchangeable.

Tabular and lite exports​

For spreadsheet tools and context-constrained agents, each build also publishes:

ArtifactUse
countries.csv.zst / intblocks.csv.zstFlattened scalar columns (structs expanded to population_value, hq_city, etc.); decompress with zstd -d
countries-lite.parquet~12 columns: code, name, iso3code, wikidata_id, entity_type, code_status, un_status, independent, region_id, subregion, ioc_code, fifa_code
intblocks-lite.parquet~9 columns: id, name, status, wikidata_id, geographic_scope, scope_category, blocktype, membership_count, legal_status
countries.json.zst / intblocks.json.zstSingle JSON arrays (same records as JSONL), zstd-compressed
datapackage.jsonFrictionless Data Package resource index

Lite variants share primary keys with full exports — join on code or id to hydrate heavy fields.

Versioning and stability​

Every build writes a manifest with:

  • version — semver (matches git tag on releases)
  • schema_hash — changes on breaking schema migrations
  • build_date, git_commit, row_count, data_license

Query version from DuckDB:

SELECT dataset, version, schema_hash, build_date FROM _meta;

Before upgrading:

  1. Compare schema_hash in your cached manifest vs the new release
  2. Read CHANGELOG.md for migration notes
  3. Apply intblock alias remaps from intblocks_aliases.json if joining on intblock id
  4. Apply country code remaps from countries_aliases.json if joining on country code (e.g. legacy KV → XK)

Dataset SemVer (what is MAJOR vs MINOR vs PATCH for this data product), alias retention, and API posture are defined in versioning-policy.md.

Stable join keys: country code and intblock id. When an intblock id is renamed, the old id appears in intblocks_aliases.json:

import json

aliases = {
    a["alias"]: a["target"]
    for a in json.load(open("data/datasets/intblocks_aliases.json"))
}
resolved = aliases.get("ASF", "ASF")  # -> "FSA"

A reason of disambiguated means the alias string now refers to a different entity (not just a rename).

Entity model​

Countries​

256 country and territory records including 249 current ISO 3166-1 assignments plus 7 non-standard entries with explicit code_status. See country-code-policy.md.

Key classification fields:

FieldUse
code_statusofficial_iso3166_1, user_assigned, obsolete, exceptionally_reserved
entity_typesovereign_state, dependent_territory, disputed_territory, etc.
un_member, un_status, independentUN participation (member / observer / non_member) plus booleans
recognition_statusOptional struct for dispute/recognition metadata

Filter current ISO countries (249 records):

df[df["code_status"] == "official_iso3166_1"]

Intblocks​

Organizations, treaties, alliances, federations, and similar groupings. Each record has:

  • id — stable uppercase identifier (join key)
  • blocktype — list of taxonomy keys (e.g. intorg, trade, military)
  • includes — membership list; includes[].id is authoritative for joins
  • status, founded, dissolved, predecessor, successor — lifecycle
  • wikidata_id, description, tags, topics — linking and discovery
  • partof — parent organization ids

Member entry shape: {id, name, type, status, joined, left, role, note}. Use id for joins; name is a source label and may not match the canonical country name. left is the departure date (ISO string) when the member left.

Membership status (includes[].status)​

Canonical catalog: data/schemas/includes_status.yaml.

StatusMeaning
member / founding_memberCurrent (or founding) full member
observer / associated_observerObserver seat
associate / associate_member / associatedAssociate-tier participation
former_memberLeft; prefer left date when known
participant / partner / cooperatingNon-member participation
recipient / contributor / funderProgram / funding roles
represented / validation / extensionSpecial coverage statuses

Filter current members with status NOT IN ('former_member') (and usually exclude observers unless the question asks for them).

Intblock scope_category​

Optional inclusion taxonomy (igo, treaty_body, policy_forum, reference_enumeration). reference_enumeration covers named geographic/set groupings (e.g. SIDS), not country attribute partitions (those live on countries — see below). See intblock-inclusion-policy.md. How to search catalogues and improve records: discovery.md. Catalogues searched and roster authorities used to compile membership: intblock-sources.md.

SELECT id, name FROM intblocks WHERE scope_category = 'igo' ORDER BY id;

Country attribute fields (former attribute intblocks)​

Driving side, scripts, DVD region, broadcast standards, legal traditions, and rail gauges are country columns. Remap retired intblock ids with data/datasets/attribute_intblock_migrations.json.

-- Was: members of RHTRAFFIC / LHTRAFFIC
SELECT code, name FROM countries WHERE car_side = 'left' ORDER BY code;

SELECT code, name, dvd_region FROM countries WHERE dvd_region = 1;

-- List-of-struct attributes: unnest and filter on id
SELECT c.code, c.name
FROM countries c, UNNEST(c.writing_directions) AS t(d)
WHERE d.id = 'rtl';

SELECT c.code, c.name, g.gauge_mm
FROM countries c, UNNEST(c.rail_gauges) AS t(g)
WHERE g.id = 'russian' AND g."primary" = true;

Vocab catalogs: data/vocabs/. Coverage is sparse for some fields (migrated from attribute-partition intblocks). Government-form typology is vocab-only (government_forms.yaml) and is not assigned on country records. Verified recipes: query-examples.md.

Country crosswalk ids​

Optional join aids: geonames_id, ioc_code, fifa_code, fips_code, and bbox.

SELECT code, name, ioc_code, fifa_code FROM countries
WHERE code_status = 'official_iso3166_1' AND ioc_code IS NOT NULL;

OpenAI-style tool schemas​

Copy-ready function definitions for tool-calling agents:

Schema migration files​

When a release changes JSON Schema properties, data/datasets/migration.vX.Y.Z.json lists added/removed/type-changed fields per dataset. Compare against your cached schema_hash before upgrading.

Blocktypes​

Taxonomy definitions for intblock categories. Join intblocks.blocktype values to blocktypes.id.

Field semantics (countries)​

Structured metrics​

population, area, and gini are structs, not plain numbers:

{value: number, year: integer|null, source: string, source_id: string}
  • Use .value for the numeric field
  • year is null when the source year is unknown (never 0)
import pandas as pd

# .struct requires ArrowDtype-backed columns
df = pd.read_parquet("data/datasets/countries.parquet", dtype_backend="pyarrow")
pop = df["population"].struct.field("value")
year = df["population"].struct.field("year")

# default backend: struct columns are Python dicts
df = pd.read_parquet("data/datasets/countries.parquet")
pop = df["population"].apply(lambda v: v["value"] if v is not None else None)

World Bank classifications​

region, adminregion, incomeLevel, lendingType are {id, value} structs. Filter on the stable id, not on value — labels vary upstream (some regions carry an (all income levels) suffix). region/incomeLevel/lendingType are absent for 8 entities the World Bank does not classify (overseas territories, special statistical areas, non-standard codes); adminregion is absent for 39 records because it only covers low- and middle-income economies. Do not treat missing values as data errors.

Borders​

borders is a list of ISO 3166-1 alpha-3 land-neighbor codes (e.g. CAN, MEX), not alpha-2. Island nations may have empty borders.

Names and aliases​

FieldPurpose
nameWorld Bank-style English short name (may lag official short-form renames)
official_nameFormal long-form state name
common_namesModern short forms and aliases for fuzzy matching
other_namesTranslations {id, name}
native_namesMap of lang code → {official, common}

For entity linking, search across name, common_names, other_names, and codes.

Provenance​

Country records may include provenance: list of {field, source, retrieved_at, url, license}. Use this to assess data freshness and attribute upstream sources. See ATTRIBUTION.md.

Field semantics (intblocks)​

FieldNotes
includes[].idAuthoritative member identifier (usually country code)
includes[].typeMember type (country, organization, etc.)
includes[].statusCatalogued in includes_status.yaml — see table above
includes[].joined / includes[].leftISO dates; left is exported on JSONL, Parquet, DuckDB, and memberships
membership_countDeclared count; compared to the country roster unless membership_count_type is set
membership_count_typeUnit of the count (countries default; companies, individuals, …)
headquarters{city, country, coordinates} — optional; coverage varies
legal_status, geographic_scopeOptional free text / enum; legal_status is not a controlled vocabulary

Query recipes​

Full catalog with verified row counts: query-examples.md.

DuckDB struct lists: UNNEST(column) AS t(row) then row.field (e.g. UNNEST(i.includes) AS t(m) → m.id).

DuckDB: UN members​

SELECT code, name
FROM countries
WHERE un_member = true
ORDER BY name;

DuckDB: land neighbors of Thailand​

borders stores alpha-3 codes — join on iso3code, not code.

SELECT n.code, n.name
FROM countries th,
     UNNEST(th.borders) AS b(neighbor_iso3)
JOIN countries n ON n.iso3code = b.neighbor_iso3
WHERE th.code = 'TH'
ORDER BY n.name;

DuckDB: organizations that include Laos​

Join on includes[].id, not includes[].name.

SELECT i.id, i.name, m.status
FROM intblocks i, UNNEST(i.includes) AS t(m)
WHERE m.id = 'LA' AND m.type = 'country'
ORDER BY i.name;

DuckDB: org members (NATO)​

SELECT i.id, i.name, m.id AS member_code, m.name AS member_label
FROM intblocks i, UNNEST(i.includes) AS t(m)
WHERE i.id = 'NATO' AND m.type = 'country';

DuckDB: countries in a World Bank region​

SELECT code, name, region.id AS region_id
FROM countries
WHERE region.id = 'ECS'
  AND code_status = 'official_iso3166_1';

Pandas: resolve intblock alias before join​

import json
import pandas as pd

aliases = {a["alias"]: a["target"] for a in json.load(open("data/datasets/intblocks_aliases.json"))}
blocks = pd.read_parquet("data/datasets/intblocks.parquet")
blocks["id"] = blocks["id"].map(lambda x: aliases.get(x, x))

Access paths​

MethodWhen to use
internacia.duckdbSQL analytics, multi-table joins, version check via _meta
ParquetPandas/Polars/Arrow pipelines
JSONL.zstStreaming, language-agnostic JSON consumers
internacia-pythonTyped lookups, fuzzy search, filters
internacia-apiSelf-host only — no public hosted Internacia HTTP API
Source YAMLMaintainers only — editing and validation

Decompress zstd: zstd -d data/datasets/countries.jsonl.zst

Licensing and attribution​

Internacia is part of the Dateno open-source project.

  • Data and documentation: CC BY 4.0 — see DATA_LICENSE
  • Code: MIT — see LICENSE
  • Upstream: World Bank (CC BY 4.0), Wikidata (CC0), IANA tzdata (public domain), mledoze/countries (ODbL-1.0, centroid field only — see the compatibility note in ATTRIBUTION.md)
  • DOI: 10.5281/zenodo.21452328 (Zenodo concept DOI, always resolves to the latest version); machine-readable metadata in CITATION.cff

When redistributing or citing, cite the DOI plus the version from the manifest, and credit upstream sources per ATTRIBUTION.md.

Common mistakes​

  1. Parsing YAML sources instead of exported datasets — slower, may miss build-time normalization
  2. Joining intblocks on includes[].name — use includes[].id
  3. Using alpha-2 in borders joins — borders are alpha-3; join on iso3code
  4. Treating missing World Bank fields as errors — expected for territories outside WB taxonomy
  5. Assuming all 256 codes are ISO official — filter on code_status
  6. Ignoring schema_hash on upgrade — structured fields and types change between releases
  7. Assuming internacia-api is a hosted service — it is self-host only; use local DuckDB or the Python SDK