ChatGPT disclosed things in this session that I did not expect it to disclose, and there is no guarantee it stays available. I would rather people had time to use it than had a tidier version of it later.
So: every word of every ChatGPT response here is verbatim, and that part is checked automatically on every build. What is not finished is the presentation. The color coding on the code blocks is incomplete and some of it is imprecise, and there are notes to myself still sitting in the page.
Parts 1 and 2 are close to empty and are getting a lot of detail over the next week or so, along with a cleanup pass on everything else. Worth checking back.
Evidence and sourcing
What ChatGPT Copied and What It Converted
Point by point, which parts were transcriptions of real responses and which were built afterwards.
Now we can ask it to go back through the previous answer and say, point by point, which parts are copies of what came back and which parts are its own conversion.
It does, in a table. That table is where the color key on this site comes from.
It also corrects its own wording. It had been calling the returned text "exact," which was too strong, because it had swapped session IDs for placeholders and cut some long passages. Placeholder IDs still count as exact here. Cut text does not.
A short, precise answer, and the one that makes everything before it usable.
The mapping table
It produces a table mapping each label it had been using to what that label actually was:
- Actual request means the JSON arguments genuinely sent to the tool.
- Exact model-visible format means the returned representation, though
sometimes shortened or with placeholders.
- Lossless normalized JSON means JSON it created, by parsing and restructuring
the visible response.
- Documented public JSON means a schema from OpenAI's documentation.
- Proposed fields mean fields added to illustrate something useful, never
observed.
Then it goes family by family. For web, news, academic and Reddit results, what came back was text blocks, and the JSON underneath them was entirely its own restructuring. For products, structured-looking labeled text that was not JSON. For finance, a prose sentence, parsed afterwards. For sports schedules, one plain text line per game. For opened pages, a text header followed by numbered lines.
Weather is the unusual case
Its alerts arrived as constructor syntax with named arguments and single-quoted strings, which is not JSON but is clearly a serialized object. When it converted that to JSON, the property names came straight from what was visible.
That makes it the strongest evidence in the conversation of a real internal shape.
The correction
It also corrects itself. Calling those response sections "exact model-visible format" was too strong, because it had replaced reference IDs, shortened long content, used ellipses, substituted a placeholder where an image sat, and removed repeated material. The better label, it suggests, is a close transcription of the model-visible response.
Two of those five changes are cosmetic. Nobody needs the real session ID, and an image cannot be printed as text. The other three do remove content, and where they happen it is visible in the block.
The line that should govern how anyone reads the rest: the normalized JSON in every section was an extra step, not the internal format received.
So your request goes out as JSON, and what comes back is closer to plain text with some light XML-style markup, sometimes with a little JSON in it. Not quite markdown, though it can contain markdown. The sports schedule is just newlines and standard timestamps, which is neither markdown nor JSON.
When you say exact model visible format, I want to be sure that is exactly what you received when you made the request.
And when you say lossless normalized JSON, that is you taking what came back and putting it into JSON for me. It is not what you receive internally and not what you work with. Is that correct, or is the lossless normalized JSON actually what you receive?
You also wrote "alert fields concretely observed" in places, followed by JSON. That reads as though the JSON is what you received. But when you show the actual model visible format, that is a different thing, although the severe weather alerts section does look like it might genuinely be structured.
Go through your response above and tell me clearly what is exactly what you received and what is you reformatting it for me.
Be concise. A list is fine. For each section: this is exactly what I received, or this is me reformatting it into JSON for you, or this JSON does represent something genuinely structured, as with the weather alerts.
Yes. The distinction is:
| Label from my previous response | What it actually was |
|---|---|
| Actual request | JSON arguments actually sent to the tool |
| Exact model-visible format | The returned representation visible to me, but sometimes shortened, redacted, or represented with placeholders |
| Lossless normalized JSON | JSON I created for you by parsing and restructuring the visible response |
| Fields concretely observed | Field names or values visibly present in the response, rewritten by me as JSON for clarity |
| Documented public JSON | Schema from OpenAI’s public documentation, not an internal response I received |
| Proposed/unknown fields | Fields added to illustrate what might be useful, not observed |
By result type
General web, news, academic, and Reddit
- Returned to me: Textual result blocks containing titles, URLs, reference IDs, snippets, dates, authors in some academic results, votes in some Reddit results, and similar information.
- The displayed text blocks: Close representations of what I saw, but some were shortened with
..., generalized, or had reference IDs replaced. - The JSON beneath them: Entirely my restructuring. I did not receive those cards as JSON.
Image results
- Returned to me: An actual rendered image plus associated textual source information.
<RENDERED IMAGE>was my placeholder indicating where the actual image appeared.- The normalized JSON: My representation. I did not receive an object containing
rendered_image.available_to_model.
Product results
- Returned to me: Structured-looking text with labeled sections such as
Desc,Rating,Merchants, andSpecs. - It looked somewhat record-like, but it was not JSON.
- The JSON version: My conversion of those labeled sections.
Finance
- Returned to me: A prose sentence containing the name, ticker, price, change, volume, market cap, P/E ratio, and other values.
- Not returned as JSON.
- The finance JSON: My parsing of that sentence.
Sports schedule
- Returned to me: One plain-text line per game.
Yankees @ Phillies on Sunday, Jul 26, 2026 at 04:20 PM PDT- Not JSON and not meaningfully Markdown.
- The events array: My conversion.
Sports standings
- Returned to me: Markdown-like headings and plain-text team records.
# Conference: American League
## Division: East
Tampa Bay Rays 61-43- The nested conference/division JSON: My conversion.
Calculator and time
- Returned to me: One formatted plain-text sentence.
- The JSON: My conversion.
Opened webpages, find, click, and PDFs
- Returned to me: A structured textual header followed by numbered lines, for example:
Content type: text/html;
Source: open({...});
Total lines: 9628
L1529: ResponseFunctionWebSearch = object ...- This is a model-facing textual serialization, not raw JSON.
- The objects containing
source_operation,total_lines, andlines: My JSON conversion.
PDF screenshots
- Returned to me: A rendered page image associated with a PDF reference.
- The JSON describing the rendered image: My representation, not an actual returned object.
Errors
- Returned to me: Structured plain text containing the failed operation, URL, status, and error message.
- The error JSON: My conversion.
Weather is the unusual case
The weather response contained ordinary prose plus something resembling a Python object representation:
SevereWeather(
start_timestamp=1784826000,
end_timestamp=1785207600,
utc_offset_sec=-25200.0,
name='Buckeye/Avondale',
summary='...',
details='...'
)That constructor-style text was genuinely visible in the result.
It was not JSON because it used:
SevereWeather(...)- Named constructor arguments
- Single-quoted strings
- Python-like object syntax
When I subsequently showed:
{
"start_timestamp": 1784826000,
"end_timestamp": 1785207600,
"utc_offset_sec": -25200.0,
"name": "Buckeye/Avondale"
}that JSON was my conversion. However, unlike some other normalized fields, its property names came directly from the visible SevereWeather(...) representation.
One correction to my wording
Calling all of those sections “exact model-visible format” was too strong because I sometimes:
- Replaced reference IDs
- Shortened long content
- Used
... - Substituted
<RENDERED IMAGE> - Removed repetitive material
- Slightly regularized whitespace
A better label would have been:
Close transcription of the model-visible response
The normalized JSON in every section was an extra step performed for your benefit. It was not the internal format I received.