Entity Charts

Every day we publish an edition of each entity chart: a ranked list of the knowledge-graph entities podcasts talked about most over the window. The entertainment charts (tv-series, movies, video-games) order entities by how many distinct shows mentioned them in ordinary dialogue. The sports charts (nfl, nba, mlb, nhl, college-football, wnba, college-basketball, soccer, golf, tennis, formula-1, nascar, pro-wrestling) rank the players, teams, coaches, and events of one league or sport together. The composite charts blend reach with each entity’s 52-day elevation, so a story that is unusually big for its subject can outrank a perennial with more shows: all draws on a wider curated pool of entity types — people, books, music, brands, companies and organizations, sports, events, products, and more, as well as the entertainment categories — while all-entertainment and all-sports pool only their own group’s charts. The guests chart ranks people by the distinct shows they appeared on. Each entry carries its movement since the previous edition; on the mention charts’ weekly editions, a momentum classification as well; and — once the narrative pipeline has run and there is something worth explaining — a one-line explanation of why it moved, backed by a verbatim quote you can play from the source episode. Fields that do not apply to an entry are omitted from the JSON rather than sent as null. Think of a music chart where the artists are entities and the airplay is podcast dialogue. The entertainment charts are volume-ranked: an entity that is mentioned on hundreds of shows every week sits near the top every week. The composite and league charts temper that reach with elevation rather than replacing it. Acceleration is a separate axis, reported as accel_multiple on entries with enough history (never on the guests chart) and available as an ordering with sort=acceleration, so a from-zero breakout deep in the chart can be found without it having to outrank the perennials. Use these endpoints when you want to:

Render a “what podcasts are talking about” leaderboard, overall, per group, or per category or league.
Track an entity’s chart position and its movement since the previous day’s edition.
Surface the story behind a move with a receipt: the explanation, the quote, the episode, and the offsets to play it.
Find the guests appearing across the most shows this week, with their appearances and a highlight clip.

List the charts

curl "https://api.particle.pro/v1/podcasts/entities/charts" -H "X-API-Key: $PARTICLE_API_KEY"

Response (truncated)

{
  "data": [
    {
      "category_slug": "tv-series",
      "category_name": "TV Series",
      "source": "mentions",
      "group": "entertainment",
      "window": "7d",
      "windows": ["7d", "24h"],
      "edition_date": "2026-09-08",
      "status": "enriched",
      "leader": {
        "type": "entity",
        "entity": { "id": "1GmOP1C3LpXJ3HOcB0", "slug": "the-odyssey", "name": "The Odyssey", "type": { "slug": "movie", "name": "Movie" } }
      },
      "computed_at": "2026-09-08T10:27:33Z",
      "enriched_at": "2026-09-08T10:30:14Z"
    },
    { "category_slug": "all-entertainment", "category_name": "All Entertainment", "source": "mentions", "group": "entertainment", "windows": ["7d"], "..." : "..." },
    { "category_slug": "movies", "category_name": "Movies", "source": "mentions", "group": "entertainment", "..." : "..." },
    { "category_slug": "video-games", "category_name": "Video Games", "source": "mentions", "group": "entertainment", "..." : "..." },
    { "category_slug": "guests", "category_name": "Guest Appearances", "source": "guest_appearances", "group": "overall", "..." : "..." },
    { "category_slug": "all", "category_name": "All Entities", "source": "mentions", "group": "overall", "windows": ["7d"], "..." : "..." },
    { "category_slug": "all-sports", "category_name": "All Sports", "source": "mentions", "group": "sports", "windows": ["7d"], "..." : "..." },
    { "category_slug": "nfl", "category_name": "NFL", "source": "mentions", "group": "sports", "windows": ["7d", "24h"], "..." : "..." }
    // …nba, mlb, nhl, college-football, then wnba, college-basketball, soccer, golf, tennis,
    // formula-1, nascar, pro-wrestling with "windows": ["7d"]
  ]
}

Every chart lists with the headline state of its current edition and the entity at rank 1. Pass window=24h to summarize the daily editions instead of the weekly ones. The category_slug is the {category} path parameter of the chart endpoint. group puts each chart in a directory section (overall, entertainment, or sports), so you can build tabs without a hard-coded chart list. windows lists the windows the chart publishes. leader is omitted on charts the caller may not read (see Access).

Chart	`group`	`source`	Ranks	Windows
`all`	`overall`	`mentions`	A curated cross-category pool of entity types (people, books, music, brands, organizations, sports, events, products, and more — wider than the entertainment categories); ranked by a blend of reach and 52-day elevation, not by `podcast_count` alone	`7d` only
`guests`	`overall`	`guest_appearances`	People, by the shows they appeared on	`7d`, `24h`
`all-entertainment`	`entertainment`	`mentions`	The entries of `tv-series`, `movies`, and `video-games` pooled; ranked by the same blend as `all`	`7d` only
`tv-series`, `movies`, `video-games`	`entertainment`	`mentions`	Entities of that type, by `podcast_count`	`7d`, `24h`
`all-sports`	`sports`	`mentions`	The entries of every sports chart pooled; ranked by the same blend as `all`, with the league charts’ top-10 rules (see League chart entries)	`7d` only
`nfl`, `nba`, `mlb`, `nhl`, `college-football`	`sports`	`mentions`	The players, teams, coaches, and events of that league; ranked by a blend of reach and 52-day elevation (see League chart entries)	`7d`, `24h`
`wnba`, `college-basketball`, `formula-1`, `nascar`, `pro-wrestling`	`sports`	`mentions`	The same, for that league	`7d` only
`soccer`, `golf`, `tennis`	`sports`	`mentions`	The same, for the whole sport; `golf` and `tennis` also rank the majors and Grand Slams	`7d` only

Get a chart

curl "https://api.particle.pro/v1/podcasts/entities/charts/all?limit=10" \
  -H "X-API-Key: $PARTICLE_API_KEY"

Response (truncated)

{
  "category_slug": "all",
  "category_name": "All Entities",
  "source": "mentions",
  "window": "7d",
  "edition_date": "2026-09-08",
  "status": "enriched",
  "methodology_version": 1,
  "computed_at": "2026-09-08T10:27:34Z",
  "enriched_at": "2026-09-08T10:34:36Z",
  "entries": [
    {
      "rank": 1,
      "subject": {
        "type": "entity",
        "entity": {
          "id": "17PzxGO3qko0j2",
          "slug": "steve-witkoff",
          "name": "Steve Witkoff",
          "description": "United States Special Envoy to the Middle East",
          "type": { "slug": "person", "name": "Person" }
        }
      },
      "breaking": true,
      "signals": {
        "state": "breaking",
        "trend_recent": 148.84,
        "trend_baseline": 17.72,
        "elevation_recent": 65.24,
        "elevation_baseline": 20.46
      },
      "previous_rank": 1,
      "movement": "flat",
      "rank_change": 0,
      "podcast_count": 269,
      "episode_count": 783,
      "mention_count": 1149,
      "accel_multiple": 1.86,
      "previous_podcast_count": 112,
      "previous_episode_count": 143,
      "previous_mention_count": 183,
      "why": {
        "text": "Steve Witkoff and Jared Kushner traveled to Moscow on September 5, 2026 and then to Kyiv by September 7 for Russia-Ukraine peace talks with Putin and Zelensky.",
        "quote": "Jared Kushner and Steve Witkoff came right from Moscow. They said they want peace talks to restart.",
        "verifiable": true,
        "source_episode_id": "2xOeI5vaV1zZ2VOXwdeLNq",
        "source_episode_name": "Miami Plane Crash, Witkoff & Kushner Visit Ukraine, Far-Right AfD Wins In Germany",
        "source_episode": {
          "id": "2xOeI5vaV1zZ2VOXwdeLNq",
          "slug": "miami-plane-crash-witkoff-kushner-visit-ukraine-far-right-afd-wins-in-germany",
          "title": "Miami Plane Crash, Witkoff & Kushner Visit Ukraine, Far-Right AfD Wins In Germany",
          "published_at": "2026-09-07T09:52:20Z",
          "podcast": { "id": "2vCpLiRTlJsRMghWU2OPfL", "slug": "up-first-from-npr", "title": "Up First from NPR" }
        },
        "quote_start_seconds": 337.6,
        "quote_end_seconds": 343.68,
        "quote_window": [
          { "number": 69, "speaker": "Michel Martin", "role": "HOST", "start_seconds": 332.38, "end_seconds": 337.2, "text": "President Trump's envoys went to Ukraine's capital, Kyiv, for the first official visit during wartime." },
          { "number": 70, "speaker": "Michel Martin", "role": "HOST", "start_seconds": 337.6, "end_seconds": 343.68, "is_mention": true, "text": "Jared Kushner and Steve Witkoff came right from Moscow. They said they want peace talks to restart." }
        ]
      }
    }
    // …9 more entries
  ]
}

Query parameters

Parameter	Default	Description
`window`	`7d`	`7d` or `24h`. The composite charts (`all`, `all-entertainment`, `all-sports`) publish `7d` only; asking for one with `window=24h` is a `422`.
`limit`	`10`	Entries to return, from rank 1 down, up to 100. Editions persist far deeper than ten, so raise this to read further into the chart. A session without paid Radar access reads the `all` chart 10 entries deep regardless (see Access).
`sort`	`rank`	`rank` returns the chart as published. `acceleration` reorders the whole edition by `accel_multiple`, fastest first, so a fast riser deep in the chart surfaces; each entry still carries its published `rank`. Entries with no multiple, or fewer than 5 podcasts, follow in rank order. The guests chart ignores it.
`include`	—	Comma-separated optional sections. `timeseries` adds each entry’s daily history; `driven_by` names, on a derivative entry, the entry whose coverage drives it.

An unknown {category} is a 404 that points you back at the list endpoint.

Reading an entry

Field	Meaning
`rank`, `previous_rank`, `movement`, `rank_change`	Position now and on the previous edition. Editions are daily, so on a `7d` chart this is the rolling weekly window compared with yesterday’s, not with the week before. `movement` is `new`, `up`, `down`, or `flat`; `rank_change` is positions climbed (negative when it fell). A debut has `movement: "new"`, no `previous_rank`, and `rank_change: 0` — read `movement` to tell a debut from a flat hold.
`subject`	The ranked thing. `type` says which member is set: `entity` (a knowledge-graph entity, valid on the entity endpoints) or `person` (valid on the person and guest endpoints). Both are absent on a `tombstone` — an entity deleted upstream after publication; the position and counts stay as history.
`podcast_count`	On the mention charts, distinct podcasts that mentioned the entity organically in the window; on the guests chart, distinct shows the person appeared on. The ranking key on the entertainment category charts and the guests chart. On the composite and league charts, rank blends this reach with 52-day elevation, so a lower count can sit above a higher one.
`episode_count`, `mention_count`	Distinct episodes and individual dialogue mentions. Visible for context, never used to rank: recap networks re-airing one clip inflate them.
`previous_*_count`	The same three counts over the window immediately before this one. Always emitted; a debut reads 0.
`accel_multiple`	How much faster the entity’s coverage grew on the last complete day than its own trailing six-day baseline, normalized for daily publishing volume. Same scale as the trends endpoint. Absent on a debut or with too little history, and always absent on the guests chart.
`breaking`	Coverage is spiking sharply against the entity’s own recent baseline and stayed active in the last day.
`signals.state`	Momentum classification: `breaking`, `rising`, `steady`, `settling` (falling, but the week is still far above its long-run norm — a big story past its peak), `cooling`, or `new`. Render an unknown value as `steady`. `signals` is absent on the guests chart and on `24h` editions.
`signals.trend_`, `signals.elevation_`	The window averages behind the state: the weighted mean of the last 3 complete days against the 4 before them (trend), and the last 7 days against the 52 before them (elevation). Normalized for publishing volume and the entity’s weekly cycle, so they will not equal the plain mean of the timeseries buckets; draw them as reference lines, not headline numbers. Omitted, with `state` kept, for a session without paid Radar access (see Access) and when `state` is `new`.
`why`	The explanation, when one was written. `text` is the one-liner; `quote` is the verbatim dialogue when one was captured, and is absent otherwise. `verifiable` says whether a quote was captured and its source episode still exists; gate quote display and playback on it. When true, `source_episode_id` and `source_episode_name` identify the episode, `quote_start_seconds` / `quote_end_seconds` are the offsets to play it from, `quote_window` holds the surrounding lines with a speaker each (`is_mention: true` marks the quote’s own lines), and `source_episode` is the receipt with its podcast — loaded best-effort, so it can be absent even here; fall back to the id and name. When false, either no quote was captured (the episode fields may still be present) or the episode was deleted after the explanation was written (`source_episode`, `source_episode_id`, and `source_episode_name` are all absent) — render `text` alone. `clip` is set when an existing highlight clip contains the quote. `why` is absent for entries with nothing notable to explain, which is normal.
`driven_by`	With `include=driven_by`: the entry on the same edition whose coverage drives this one (a tribute song rising with its artist), so you can group derivative entries under what drives them. Only on derivative entries.
`timeseries`	With `include=timeseries`: `{ interval: "day", start, end, buckets: [{ start, podcast_count, mention_count }] }`, one bucket per Eastern day through the last complete day before `edition_date`.
`caveats`	Measurement caveats on the entry (the edition carries its own list).

Guest chart entries

guests has source: "guest_appearances": it ranks people by the distinct shows they appeared on in the window, so several fields read differently. subject.type is person; mention_count is always 0 and accel_multiple and signals are absent, because guest appearances are counted per episode, not per dialogue line. In their place each entry carries:

Field	Meaning
`appearances`	The guest’s episode appearances in the window, newest first (up to five inline), each a play-in link to the conversation. The full history is on `GET /v1/podcasts/guests/{id}/appearances`.
`highlight_clip`	The guest’s single highest-engagement clip from all of their appearances in the window, not just the five listed inline — `title`, `start_seconds`, `end_seconds`, `engagement`, `audio_url` when a pre-cut file exists, and the source `episode`. Present only when a scored clip exists.

League chart entries

Each sports chart ranks the members of one league, or of one sport for soccer, golf, and tennis: players, teams, coaches, other league figures, and events. On golf and tennis the majors and Grand Slams are members too. A league chart does not rank by podcast_count alone, because a team is named in every episode about any of its players. Instead it measures each member’s reach against members of the same kind (teams against teams, people against people), blends that with the whole league’s reach, and combines the result with 52-day elevation. Two more rules apply to the top 10:

League organizations, such as a division or a conference, never hold a top-10 position. They still chart, directly below the top 10.
A member that the league shares with a neighbor (a national sports broadcaster, or a coach who moved between the college and professional game) also sits below the top 10 unless most of its coverage is about this league.

subject.type is always entity. An entity that belongs to two leagues can chart on both. all-sports pools every sports chart and ranks each entity once. It measures each entity’s reach against its league and the whole pool together, so a league with less coverage does not seat its leader level with the NFL’s, and it applies both top-10 rules above.

Access

An API key reads every chart at full depth, with every field described above. A session without paid Radar access — signed out, or signed in on plans that do not yet provide it — reads a preview instead:

the composite all chart only, 10 entries deep regardless of limit;
signals keeps state but omits the trend_* and elevation_* operands;
every other chart (all-entertainment, all-sports, the category charts, the league charts, and guests) is refused, and the list endpoint omits their leader. A signed-out session gets 401 auth_required; a signed-in one gets 402 paid_plan_confirmation_required, whose resolve.action says whether to confirm the plan it holds or add Radar.

Treat the operand fields and signals itself as optional keys when rendering, and do not assume a response holds as many entries as you asked for.

Editions and windows

Charts are recomputed once a day. An edition’s ranking and counts are fixed at computation, but the narrative pipeline keeps writing to it afterwards: status, enriched_at, and entries’ why fill in over the following minutes, so re-read a computed edition rather than caching it for the day. edition_date is the Eastern-time calendar date the edition describes; computed_at is when positions were fixed. status is computed while the narrative pipeline is still running and enriched once the pipeline has processed every candidate — entries it skipped still have no why, and an entry’s why may already be present on a computed edition. methodology_version increments when the ranking formula changes; editions across a version change are not directly comparable. Two windows are published for the category and guests charts and the nfl, nba, mlb, nhl, and college-football charts: 7d (the default, a weekly view) and 24h (a daily view). The composite charts (all, all-entertainment, all-sports) and the other sports charts are weekly only.

How the charts are computed

Distinct podcasts, not episodes. One show that reads a book aloud over ninety episodes counts once. Episode and mention counts are shown but never sort.
Organic mentions only. An episode counts when its dialogue names the entity outside ad reads. This is the same gate the mentions endpoint applies, so following an entry through to its mentions always finds results.
Read from persisted editions. A request never touches raw mention data, so reads are fast and yesterday’s chart is exactly what yesterday’s readers saw.
Explanations carry receipts. A why that quotes an episode names it and, while that episode still exists (verifiable), links it with playback offsets; an explanation written without a quote carries text alone. Entities whose mentions look mislinked are flagged in caveats, not silently dropped.

Choosing the right endpoint

You want…	Use
What podcasts talked about most this week, by category or league, with movement and explanations	`GET /v1/podcasts/entities/charts/{category}` (this page)
The biggest sports stories this week across every league	`GET /v1/podcasts/entities/charts/all-sports`
The fastest risers this week, wherever they rank	`GET /v1/podcasts/entities/charts/all?sort=acceleration`
Every episode and line where one entity is named	`GET /v1/podcasts/mentions`
How often one entity is mentioned over time	`GET /v1/podcasts/mentions/timeseries`
A guest’s full appearance history and stats	`GET /v1/podcasts/guests/{id}`

Getting started

Recipes

Podcasts

Companies

Knowledge graph

Alerts

Account

Errors

List the charts

Get a chart

Query parameters

Reading an entry

Guest chart entries

League chart entries

Access

Editions and windows

How the charts are computed

Choosing the right endpoint

​List the charts

​Get a chart

​Query parameters

​Reading an entry

​Guest chart entries

​League chart entries

​Access

​Editions and windows

​How the charts are computed

​Choosing the right endpoint

List the charts

Get a chart

Query parameters

Reading an entry

Guest chart entries

League chart entries

Access

Editions and windows

How the charts are computed

Choosing the right endpoint