# Actor Dataset Provenance Enricher (`flintglade/actor-dataset-provenance-enricher`) Actor Enrich Apify dataset rows with deterministic field-level provenance, source identity, canonical hashes, lineage, rights indicators, and artifact manifests. - **URL**: https://apify.com/flintglade/actor-dataset-provenance-enricher.md - **Developed by:** [Flintglade](https://apify.com/flintglade) (community) - **Categories:** Developer tools, Automation, Other - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $1.00 / 1,000 enriched rows This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## Actor Dataset Provenance Enricher Append a stable, versioned provenance object to rows from inline input or user-selected Apify datasets while preserving each original JSON object. ### What this does not do - It does not correct source data. - It does not make a rights or legal determination. - It does not automatically discover personal data. - It does not mutate the original dataset. - It does not use an external database. - It does not claim that unknown provenance is complete. Provenance enrichment records available evidence. It does not independently validate source claims or rights. ### Source classes and authorization The Actor reads either 1-100 inline objects or 1-5 datasets explicitly selected through Apify's read-authorized resource picker. It does not discover datasets, crawl URLs, or mutate an upstream dataset. Dataset reads are ordered, offset-addressed, bounded pages. Before each page, the page limit is reduced to the remaining row and paid-event capacity; after Apify reports the charge cap, no next page is requested. Five named profiles are supported: AP-003, AP-014, AP-021, AP-027, and AP-002. `auto` accepts only explicit `/actor/id`, `/actorId`, or `/schema/actorId` markers. A dataset name is never treated as an Actor ID. Unknown rows require explicit `fieldMappings` and use the `generic` profile. New provider fields remain under `_provenance.unmappedSourceFields` until deliberately mapped. ### Inputs Select exactly one of `datasetIds` and `inlineItems`. `includeOriginal` must be true. `maxRows` defaults to 10,000 and is capped at 100,000. `startOffset` resumes at a deterministic zero-based row offset; when several datasets are selected, that same offset is applied independently to each dataset. Processing concurrency is one in version 0.1. `default_and_named` mirrors each successfully written enriched row to `outputDatasetName`; the paid default-dataset write remains authoritative. Small public example: ```json { "inlineItems": [{ "recordType": "result", "actor": {"id": "AP-003", "name": "scholarly-correction-monitor"}, "entity": {"key": "10.1038/s41586-021-03819-2"}, "sources": [{"sourceId": "crossref", "retrievedAt": "2026-07-14T00:00:00Z"}], "data": {"status": "fixture"} }], "profile": "auto", "includeOriginal": true, "maxRows": 10 } ```` Input echoes hash private dataset IDs and inline content. No private storage token or row body is copied into the run manifest. ### Outputs Each admitted row contains a deterministic `enrichedRowId`, the unchanged JSON object under `original`, and `_provenance`. Provenance includes the selected profile and evidence decision; available Actor, run, dataset, source, timestamp, rights, status, and lineage fields; raw and canonical original hashes; a non-recursive enriched-content hash; normalizer version; field-level `observed`, `derived`, `user_supplied`, or `unknown` classifications; explicit missing/null distinction; and unmapped source fields. Example fragment: ```json { "recordType": "result", "status": "enriched", "original": {"actor": {"id": "AP-003"}, "entity": {"key": "demo"}}, "_provenance": { "schemaVersion": "1.0.0", "profile": "AP-003", "normalizerVersion": "actor-dataset-provenance-normalizer/1.0.0" }, "charge": {"event": "enriched-row", "chargeable": true, "reason": "schema_valid_enriched_row"} } ``` KVS contains `PROVENANCE_PROFILE.json`, `RUN_MANIFEST.json`, and `ARTIFACT_MANIFEST.json`. Artifact entries carry content type, byte length, SHA-256, schema version, and generation rule. The artifact manifest excludes its own bytes by an explicit non-recursive self-exclusion rule. An uncharged run-summary row reports profile counts, enriched/rejected totals, exact platform charged count, and field completeness. ### Pricing Only a schema-valid enriched row successfully written by `Actor.push_data` uses the `enriched-row` event, priced at **$0.001 per enriched row**. Ten enriched rows cost at most $0.01 in Actor events; 10,000 cost at most $10.00, plus Apify platform usage. Mapping errors, invalid JSON, denied/deleted datasets, internal hash failures, charge-limit markers, artifacts, named-dataset mirrors, and the run summary are uncharged. Unknown optional fields remain chargeable when explicitly classified as unknown. ### State and resume behavior The Actor is stateless. Deterministic IDs use dataset ID, source offset, selected profile, and canonical original hash. The canonical enriched hash separately excludes run IDs, observation timestamps, dataset offsets, retries, platform metadata, and itself. Resume with the same `startOffset`; repeated rows keep the same IDs and produce no cross-run baseline writes. ### Partial failures and negative results An unreadable dataset yields an explicit uncharged source-error row without exposing its ID. A row without a supported marker or generic mapping yields an uncharged mapping-error row with its canonical original hash and missing requirements. Missing and explicit null fields remain distinct. Failure in one selected dataset does not hide valid rows from another. A named mirror failure is counted in the summary and does not rewrite the authoritative paid default-dataset result. ### Resource limits The launch range is 256-2048 MB, with 1024 MB default and a 900-second hosted timeout. Rows are limited to 1,000,000 canonical JSON bytes and depth 40. Dataset pages are at most 1,000 rows and are further bounded by remaining rows and charge capacity. The default inline example requires no remote source call and normally completes well within five minutes. ### Support See [SUPPORT.md](SUPPORT.md). Do not include dataset contents, private IDs, signed links, or credentials in a support report. # Actor input Schema ## `inputSchemaVersion` (type: `integer`): Versioned runtime input contract. ## `datasetIds` (type: `array`): Select 1-5 Apify datasets to read. The Actor does not discover or mutate datasets. ## `inlineItems` (type: `array`): Use 1-100 small object rows for demos and health checks instead of datasetIds. ## `profile` (type: `string`): Auto uses only explicit supported actor/schema markers. Generic requires fieldMappings. ## `fieldMappings` (type: `object`): Explicit RFC 6901 pointers for an unknown dataset. At least one mapping is required when generic is selected. ## `includeOriginal` (type: `boolean`): Required true in version 0.1. The original JSON data model is retained unchanged; runtime validation rejects false. ## `outputMode` (type: `string`): Write to the default dataset only, or also mirror successfully written enriched rows to a named dataset. ## `outputDatasetName` (type: `string`): Safe Apify dataset name required only for default\_and\_named. ## `maxRows` (type: `integer`): Global row-read ceiling across selected inputs. ## `startOffset` (type: `integer`): Zero-based starting offset. For multiple datasets, the same resume offset is applied to each selected dataset. ## `concurrency` (type: `integer`): Version 0.1 is deliberately local and streaming. ## Actor input object example ```json { "inputSchemaVersion": 1, "inlineItems": [ { "recordType": "result", "actor": { "id": "AP-003", "name": "scholarly-correction-monitor" }, "entity": { "key": "10.1038/s41586-021-03819-2" }, "sources": [ { "sourceId": "crossref", "retrievedAt": "2026-07-14T00:00:00Z" } ], "data": { "status": "fixture" } } ], "profile": "auto", "includeOriginal": true, "outputMode": "default_dataset", "maxRows": 10000, "startOffset": 0, "concurrency": 1 } ``` # Actor output Schema ## `defaultDataset` (type: `string`): No description ## `provenanceProfile` (type: `string`): No description ## `runManifest` (type: `string`): No description ## `artifactManifest` (type: `string`): No description # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "inlineItems": [ { "recordType": "result", "actor": { "id": "AP-003", "name": "scholarly-correction-monitor" }, "entity": { "key": "10.1038/s41586-021-03819-2" }, "sources": [ { "sourceId": "crossref", "retrievedAt": "2026-07-14T00:00:00Z" } ], "data": { "status": "fixture" } } ] }; // Run the Actor and wait for it to finish const run = await client.actor("flintglade/actor-dataset-provenance-enricher").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "inlineItems": [{ "recordType": "result", "actor": { "id": "AP-003", "name": "scholarly-correction-monitor", }, "entity": { "key": "10.1038/s41586-021-03819-2" }, "sources": [{ "sourceId": "crossref", "retrievedAt": "2026-07-14T00:00:00Z", }], "data": { "status": "fixture" }, }] } # Run the Actor and wait for it to finish run = client.actor("flintglade/actor-dataset-provenance-enricher").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "inlineItems": [ { "recordType": "result", "actor": { "id": "AP-003", "name": "scholarly-correction-monitor" }, "entity": { "key": "10.1038/s41586-021-03819-2" }, "sources": [ { "sourceId": "crossref", "retrievedAt": "2026-07-14T00:00:00Z" } ], "data": { "status": "fixture" } } ] }' | apify call flintglade/actor-dataset-provenance-enricher --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=flintglade/actor-dataset-provenance-enricher", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Actor Dataset Provenance Enricher", "description": "Enrich Apify dataset rows with deterministic field-level provenance, source identity, canonical hashes, lineage, rights indicators, and artifact manifests.", "version": "0.1", "x-build-id": "6BDhlmbCIWvFx0FqQ" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/flintglade~actor-dataset-provenance-enricher/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-flintglade-actor-dataset-provenance-enricher", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/flintglade~actor-dataset-provenance-enricher/runs": { "post": { "operationId": "runs-sync-flintglade-actor-dataset-provenance-enricher", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/flintglade~actor-dataset-provenance-enricher/run-sync": { "post": { "operationId": "run-sync-flintglade-actor-dataset-provenance-enricher", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "includeOriginal" ], "properties": { "inputSchemaVersion": { "title": "Input schema version", "minimum": 1, "maximum": 1, "type": "integer", "description": "Versioned runtime input contract.", "default": 1 }, "datasetIds": { "title": "Authorized datasets", "minItems": 1, "maxItems": 5, "uniqueItems": true, "type": "array", "description": "Select 1-5 Apify datasets to read. The Actor does not discover or mutate datasets." }, "inlineItems": { "title": "Inline JSON rows", "minItems": 1, "maxItems": 100, "type": "array", "description": "Use 1-100 small object rows for demos and health checks instead of datasetIds.", "items": { "type": "object" } }, "profile": { "title": "Upstream profile", "enum": [ "auto", "AP-003", "AP-014", "AP-021", "AP-027", "AP-002", "generic" ], "type": "string", "description": "Auto uses only explicit supported actor/schema markers. Generic requires fieldMappings.", "default": "auto" }, "fieldMappings": { "title": "Generic JSON Pointer mappings", "type": "object", "description": "Explicit RFC 6901 pointers for an unknown dataset. At least one mapping is required when generic is selected.", "properties": { "actorId": { "title": "Actor ID pointer", "description": "Pointer to an explicit source Actor ID.", "type": "string", "pattern": "^/", "maxLength": 500 }, "runId": { "title": "Run ID pointer", "description": "Pointer to an upstream run ID.", "type": "string", "pattern": "^/", "maxLength": 500 }, "datasetId": { "title": "Dataset ID pointer", "description": "Pointer to an upstream dataset ID.", "type": "string", "pattern": "^/", "maxLength": 500 }, "sourceUrl": { "title": "Source URL pointer", "description": "Pointer to a recorded source URL.", "type": "string", "pattern": "^/", "maxLength": 500 }, "sourceIdentifier": { "title": "Source identifier pointer", "description": "Pointer to a source identifier.", "type": "string", "pattern": "^/", "maxLength": 500 }, "observedAt": { "title": "Observed time pointer", "description": "Pointer to an observation timestamp.", "type": "string", "pattern": "^/", "maxLength": 500 }, "retrievedAt": { "title": "Retrieved time pointer", "description": "Pointer to a retrieval timestamp.", "type": "string", "pattern": "^/", "maxLength": 500 }, "rightsIndicators": { "title": "Rights indicators pointer", "description": "Pointer to recorded rights indicators.", "type": "string", "pattern": "^/", "maxLength": 500 }, "upstreamStatus": { "title": "Upstream status pointer", "description": "Pointer to the upstream result status.", "type": "string", "pattern": "^/", "maxLength": 500 }, "lineageParents": { "title": "Lineage parents pointer", "description": "Pointer to recorded lineage parents.", "type": "string", "pattern": "^/", "maxLength": 500 }, "entityKey": { "title": "Entity key pointer", "description": "Pointer to the primary entity key.", "type": "string", "pattern": "^/", "maxLength": 500 } }, "additionalProperties": false }, "includeOriginal": { "title": "Preserve original row", "type": "boolean", "description": "Required true in version 0.1. The original JSON data model is retained unchanged; runtime validation rejects false.", "default": true }, "outputMode": { "title": "Output mode", "enum": [ "default_dataset", "default_and_named" ], "type": "string", "description": "Write to the default dataset only, or also mirror successfully written enriched rows to a named dataset.", "default": "default_dataset" }, "outputDatasetName": { "title": "Named output dataset", "pattern": "^[A-Za-z0-9][A-Za-z0-9._-]{0,62}$", "minLength": 1, "maxLength": 63, "type": "string", "description": "Safe Apify dataset name required only for default_and_named." }, "maxRows": { "title": "Maximum rows", "minimum": 1, "maximum": 100000, "type": "integer", "description": "Global row-read ceiling across selected inputs.", "default": 10000 }, "startOffset": { "title": "Resume offset", "minimum": 0, "maximum": 2147483647, "type": "integer", "description": "Zero-based starting offset. For multiple datasets, the same resume offset is applied to each selected dataset.", "default": 0 }, "concurrency": { "title": "Processing concurrency", "minimum": 1, "maximum": 1, "type": "integer", "description": "Version 0.1 is deliberately local and streaming.", "default": 1 } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```