The recorded ChatGPT responses are preserved. The context boxes and summaries explain how to read them.
Some code blocks remain unclassified. The labels describe the evidence shown in this session; they do not verify private system internals.
Evidence and sourcing
Reading the Code Block Labels
Identify reported tool values, illustrative examples, documented schemas, and unclassified blocks.
The code blocks include reported tool data, illustrative values, and proposed structures. Read the label with each block before using it as evidence.
Colors group the blocks by their stated source. Some blocks remain unclassified.
The recording and the underlying tool data
Two separate checks matter. A comparison with the original conversation can establish that the response was copied without edits. Establishing that a value came from an actual tool requires the corresponding tool record or other source evidence.
The existing labels are retained with the recording. Editorial notes on each page identify unresolved classifications and labels that need review because the blocks contain apparent placeholders. “Unclassified” means the source of the example has not been established, not that the response text is missing.
The labels underneath
Each block also has a text label. A green block can contain reported request arguments, transcribed results, or a later conversion using reported values.
| Label | What it means |
|---|---|
sent exactly as shown | The arguments that went to the tool |
returned exactly as shown | The text that came back from the tool |
ChatGPT JSON built from real values | Its own JSON, holding values a call returned |
ChatGPT JSON, invented values | Its own JSON, holding made-up values |
field names only, values are types | A list of which fields exist, with type words where values would sit |
OpenAI documentation | From the published API docs |
not exposed or proposed | A shape shown to say it never arrives, or one suggested for a system that does not exist |
conceptual, drawn by ChatGPT | A diagram of the process, not real functions |
unclassified, needs review | Not sorted yet |
How the session’s examples change
The earlier turns include illustrative JSON. Later turns ask ChatGPT to run tools and distinguish the results from its own formatting.
The early JSON examples are not documented private schemas. ChatGPT describes them as normalized examples and acknowledges invented values.
Then we ask whether any of it is real. ChatGPT admits the sample values were not captured from actual calls.
The later responses report tool calls with prices, scores, articles, and weather information. The notes below explain replacements described in those responses.
Use each block’s label rather than its position in the conversation to identify its stated source.
Replacements and omissions
The recorded responses describe replacing session reference IDs with placeholders like
<REF_ID>, and used <RENDERED IMAGE> where an
image sat, because an image cannot be printed as text. Where a replacement was made, the displayed block differs from the complete tool output, even if its label says “exact.”
A shortened passage or removed repetition is an omission. An ellipsis such as ... can indicate omitted text. The original tool record is needed to establish what, if anything, was removed.
One more case shows up in the later turns. ChatGPT sometimes replaced the
actual content with generic placeholders such as
<PAGE HEADING> or <AUTHOR>, because the
question at that point was about format rather than content. Those blocks show the format ChatGPT described, with values replaced. They do not independently verify the original tool format.
What an absent field establishes
When ChatGPT reports that a field is not exposed, the statement concerns the interface described in this session. It does not establish what the underlying service stores.
ChatGPT flags this itself: "Those null values mean 'not exposed,'
not that the internal system necessarily lacks them."
An ordered result list does not disclose the provider’s scoring formula. Independent verification requires documentation or logs beyond the model’s description.