By entity

Retrieve cursor-paged events that mention one canonical entity across any date window.

GET/api/v2.0-preview/entities/events

Authorizations

Bearer

Authorizationstringheader

Required Bearer API key. Programmatic access to the World API needs a key; browsing nosible.world in a browser does not.

Example: Bearer nos_sk_...

Parameters

query & path

typestringrequiredquery

NER type, or ANY to merge all indexed entity types.

Example: ORG

namestringrequiredquery

Canonical display name returned by /resolve.

Example: Nvidia

limitintegerquery

Page size. Defaults to 50, maximum 500.

Example: 50

cursorstringquery

Opaque cursor returned by the previous response.

Example: eyJ2Ijox...

fromdatequery

Inclusive lower date bound in YYYY-MM-DD format.

Example: 2026-01-01

todatequery

Inclusive upper date bound in YYYY-MM-DD format.

Example: 2026-07-20

orderenumquery

Event ordering, newest or oldest first.

Example: desc

includeenumquery

Projection selector: event_lite or event_full. This is a single query value, not an array.

Example: event_lite

include_vectorbooleanquery

Include the event embedding when the caller's tier and the selected projection allow it.

Example: false

include_livebooleanquery

Include the newest live slices when enabled for the deployment.

Example: true

How to use this endpoint

guidance

In V2.0-preview, the timeline is driven by Atlas: it lists the events that mention the canonical entity under any of its folded names, and the entity is the one the Atlas API returns. Use this route to build a company or person timeline. Resolve the canonical name first, then page with the opaque cursor; do not infer a cursor from dates or offsets. Store the as_of markers with research results.

Response body

application/json

These are the fields you can build against. Nested names use dot notation; optional sections are called out in their descriptions.

schemastring

Response schema name.

entityobject

The entity: type, canonical Atlas name and normalized lookup form. Events mentioning it under any of its names are included.

entity.namestring

Canonical Atlas name of the entity.

totalinteger

Events matching the request before paging.

countinteger

Events in this page.

orderstring

Event order: desc (newest first, the default) or asc.

date_windowobject

The window searched: from, to (defaulting to the whole archive up to index_last_date), index_last_date (the last date with indexed events), archive_cutoff (the last archive date) and include_live. With include_live=true the response adds live_events: records in the same format from the live dates after the archive cutoff, marked ephemeral and outside total and paging.

date_window.fromdate

First date searched.

date_window.todate

Last date searched.

eventsobject[]

The events, in the list format: the V2.0-preview record without its evidence. Send include=event_full for complete records, in the Event details format (without oai_vector unless include_vector=true).

events[].event_idstring

Stable event identifier.

events[].versionstring

Event record version: "2.0-preview".

events[].eventobject

The event in its original language: date, language, title, description and points.

events[].event.datedate

World archive date of the event.

events[].event.languagestring

ISO 639-1 code of the event's original language.

events[].event.titlestring

Headline, in the original language.

events[].event.descriptionstring

Short narrative, in the original language.

events[].event.pointsstring[]

Key facts of the event, one sentence each.

events[].englishobject

The event in English: title, description and points, as in Event details.

events[].coverageobject

total_coverage, total_netlocs, coverage_concentration_index, first_seen, last_seen and most_seen, as in Event details.

events[].locationsobject[]

Places of the event, its own place first, then places named in it, as in Event details.

events[].signalsobject

Sentiment, materiality, time horizon and forward-looking signals with their class probabilities, as in Event details.

events[].tickersobject[]

Listed companies the event concerns, in the Event details format without why. Empty when has_tickers is false.

events[].has_tickersboolean

Whether tickers is non-empty.

events[].entitiesobject

Named entities by NER type, {TYPE: [{name, mention_count, confidence}]}, as in Event details.

events[].ontologiesobject[]

Classifications of the event, most probable first, as in Event details.

next_cursorstring | null

Opaque cursor for the next page; send it back unchanged. null when there are no more events.

hydration_missesinteger

Events that matched but could not be read; normally 0.

as_ofobject

index_build and archive_cutoff: the index that answered. Store them with results to reproduce them.

took_msinteger

Server time in milliseconds.

languagestring[]

Languages the events were filtered to with the language parameter; empty when not filtered.

Responses and errors

HTTP

200Request succeeded. The response body is shown in the panel on the right.

400invalid_request — The date, filter, cursor, identifier, or request combination is invalid.

401api_key_required — No API key was sent. Send it as Authorization: Bearer <key> or the api-key header.

401invalid_api_key — The API key is invalid or has been revoked.

403access_denied — The requested archive date is outside the caller's World tier.

404not_found — The requested date, entity, ticker, or event does not exist in the accessible index.

410cursor_expired — The cursor belongs to a replaced index build; restart from the first page.

429rate_limited — The request exceeded the endpoint or account rate limit. Respect Retry-After when supplied.

501backend_not_configured — The World backend is not configured for this deployment.

503api_key_auth_unavailable — API key authentication is temporarily unavailable; retry shortly.

502backend_error — The BFF could not obtain a valid response from the World backend.

504backend_timeout — The World backend or an upstream data source timed out.

404unknown_entity — The requested entity is not present in the index.

{ "schema": "nosible_world_entity_events_v1", "entity": { "type": "ORG", "name": "Nvidia", "normalized": "nvidia" }, "total": 27655, "count": 50, "order": "desc", "date_window": { "from": "2010-01-01", "to": "2026-09-26", "index_last_date": "2026-09-26", "archive_cutoff": "2026-10-07", "include_live": false }, "events": [ { "event_id": "2026-09-25_ru_US_v1_doAxPSeS5x7MQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA_aF_GyRnJ", "version": "2.0-preview", "event": { "date": "2026-09-25", "language": "ru", "title": "Глава Nvidia призвал закрывать ИИ-лаборатории без контроля моделей", "description": "Дженсен Хуанг, генеральный директор компании Nvidia, выступил с призывом закрыть искусственные интеллектуальные лаборатории, не способные контролировать свои модели. Это заявление прозвучало на фоне обострения дискуссии о регулировании...", "points": [ "Дженсен Хуанг призвал закрывать ИИ-лаборатории, не способные контролировать свои модели." ] }, "english": { "date": "2026-09-25", "language": "ru", "title": "Nvidia CEO Calls for Closure of AI Labs Lacking Model Control", "description": "Jensen Huang, CEO of Nvidia, has called for the closure of artificial intelligence laboratories that are unable to control their models. This statement came amid growing debate about regulation following incidents involving rogue...", "points": [ "Jensen Huang called for the closure of AI laboratories that are unable to control their models." ] }, "coverage": { "total_coverage": 5, "total_netlocs": 5, "coverage_concentration_index": 0.236111, "first_seen": "2026-09-25T09:43:16+00:00", "last_seen": "2026-09-25T15:30:00+00:00", "most_seen": "2026-09-25T09:43:16+00:00" }, "ontologies": [ { "ontology": "media_frame", "label": "Responsibility", "probability": 1 }, { "ontology": "iab", "tier_1": "Technology & Computing", "tier_2": "Artificial Intelligence", "tier_3": "Artificial Intelligence", "tier_4": "Artificial Intelligence", "probability": 0.9797 } ], "locations": [ { "continent": "North America", "region": "North America", "country": "United States", "lat": 37.3861, "lng": -122.0839, "source": "llm", "matched_on": "{event.title}+{event.description}" } ], "signals": { "sentiment": "negative", "sentiment_prob_positive": 0.0291, "sentiment_prob_neutral": 0.4552, "sentiment_prob_negative": 0.5158, "materiality": "medium", "materiality_score": 0.566833, "time_horizon": "days", "forward_looking": "forward", "forward_looking_prob_forward": 0.5312, "forward_looking_prob_not_forward": 0.4688 }, "tickers": [ { "ticker": "NVDA.US", "companyv5_id": "c5-1b5479ea345b", "name": "NVIDIA Corporation", "website": "https://nvidia.com", "wiki_qid": "Q182477", "isin": "US67066G1040", "lei": "549300S4KLFTLO7GSQ80", "figi": "BBG000BBJQV0", "sector": "Information Technology", "industry": "Semiconductors & Semiconductor Equipment", "country_iso": "US", "exchange": "NASDAQ", "mic": "XNAS", "score": 0.994671 } ], "has_tickers": true, "entities": { "PERSON": [ { "name": "Jensen Huang", "mention_count": 7, "confidence": 1 } ], "ORG": [ { "name": "Nvidia", "mention_count": 2, "confidence": 1 } ] } }, { "event_id": "2026-09-25_en_US_v1_nYAUZGlhwMSzoAAAAAAAADHkkAAAAAAAAIQAAAAAAAA_adBvD5AM", "version": "2.0-preview", "event": { "date": "2026-09-25", "language": "en", "title": "Nvidia Raises Dividend 2,400% Yet Yield Remains Negligible for Investors", "description": "Nvidia increased its quarterly dividend by 2,400 percent, yet the payout remains minimal relative to share price. A ten thousand dollar investment yields only forty-four dollars annually. This adjustment highlights that Nvidia stock no...", "points": [ "Nvidia increased its quarterly dividend by 2,400 percent." ] }, "english": { "date": "2026-09-25", "language": "en", "title": "Nvidia Raises Dividend 2,400% Yet Yield Remains Negligible for Investors", "description": "Nvidia increased its quarterly dividend by 2,400 percent, yet the payout remains minimal relative to share price. A ten thousand dollar investment yields only forty-four dollars annually. This adjustment highlights that Nvidia stock no...", "points": [ "Nvidia increased its quarterly dividend by 2,400 percent." ] }, "coverage": { "total_coverage": 4, "total_netlocs": 3, "coverage_concentration_index": 0.433884, "first_seen": "2026-09-25T08:04:00+00:00", "last_seen": "2026-09-25T08:24:00+00:00", "most_seen": "2026-09-25T08:04:00+00:00" }, "ontologies": [ { "ontology": "media_frame", "label": "Economic Consequences", "probability": 0.9997 }, { "ontology": "asset_class", "main_class": "Equities", "sub_class": "Public Equities", "probability": 0.9904 } ], "locations": [ { "continent": "North America", "region": "North America", "country": "United States", "lat": 37.3861, "lng": -122.0839, "source": "llm", "matched_on": "{event.title}+{event.description}" } ], "signals": { "sentiment": "negative", "sentiment_prob_positive": 0.005, "sentiment_prob_neutral": 0.0371, "sentiment_prob_negative": 0.9578, "materiality": "medium", "materiality_score": 0.5, "time_horizon": "intraday", "forward_looking": "not-forward", "forward_looking_prob_forward": 0.018, "forward_looking_prob_not_forward": 0.982 }, "tickers": [ { "ticker": "NVDA.US", "companyv5_id": "c5-1b5479ea345b", "name": "NVIDIA Corporation", "website": "https://nvidia.com", "wiki_qid": "Q182477", "isin": "US67066G1040", "lei": "549300S4KLFTLO7GSQ80", "figi": "BBG000BBJQV0", "sector": "Information Technology", "industry": "Semiconductors & Semiconductor Equipment", "country_iso": "US", "exchange": "NASDAQ", "mic": "XNAS", "score": 0.993943 } ], "has_tickers": true, "entities": { "ORG": [ { "name": "Nvidia", "mention_count": 8, "confidence": 1 } ] } } ], "next_cursor": "eyJ2IjoxLCJi...", "hydration_misses": 0, "as_of": { "index_build": "inv-20261009-165952-259903", "archive_cutoff": "2026-10-07" }, "took_ms": 7, "language": [] }