# GTM-Hub meeting — 2026-09-07

Durable real-world regression case for a German GTM-Hub meeting. This is a
private real-meeting sample; do not publish or redistribute it outside the
intended development context.

## Case metadata

- **Case ID:** `gtm_hub_2026-09-07`
- **Meeting:** GTM-Hub Meeting, 2026-09-07
- **Duration:** about 78 min 36 s (4,715.52 s from existing diarization metadata)
- **Context:** a biweekly cross-functional board meeting about coordinating
  projects for new markets, products, and applications; this discussion focused
  initially on administrative questions and process definition.
- **Participants:** five confirmed participants: Malte Schnau, Henning
  Ehrenberg, Björn-Erik Falkenau, Martin Tazl, and Jovana Husemann.

## Contents and provenance

`transcript/transcript.txt` is the exact compact diarized transcript
representation supplied to both comparable Meeting Assistant protocol calls;
it is the evidentiary source for evaluation. It is byte-identical to both
source runs' `protocol/transcript_input.txt` files (SHA-256
`42ac52223cac36eeeb7c01e6d6673b2fb50973c4365b5ddaafdd12927b2f12eb`).
The source plain Whisper transcripts were also identical between the two runs,
but are not copied because they were not the exact representation supplied to
protocol generation.

| Case file | Meeting Assistant source | Generation | Speaker status |
| --- | --- | --- | --- |
| `references/meeting_assistant_anonymous_speakers.md` | `data/meetings/gtm-hub-meeting-2026-09-07/runs/b895cda2_2026-09-07_11-18-54_GTM-HUB_20260909_025239/protocol.md` | 2026-09-09 01:00:46 UTC; `qwen3.8:27b` | Diarized with anonymous `SPEAKER_XX` labels; zero confirmed speaker-to-person mappings. |
| `references/meeting_assistant_speaker.md` | `data/meetings/gtm-hub-meeting-2026-09-07/runs/284a47be_2026-09-07_11-18-54_GTM-HUB_20260909_031106/protocol.md` | 2026-09-09 01:30:44 UTC; `qwen3.8:27b` | Diarized with the same anonymous labels plus five confirmed speaker-to-person mappings in the prompt/context. |
| `references/human_protocol_malte.txt` | copied human colleague reference supplied for this case | not generated | author identified as Malte by filename; preserve unchanged. |

Both runs have meeting ID `gtm-hub-meeting-2026-09-07`, title/date
`GTM-Hub Meeting 2026-09-07`, the same source recording name and size
(154,620,897-byte FLAC), the same 4,715.52-second prepared-audio duration, the
same Whisper model (`ggml-large-v3-turbo.bin`), and the same protocol model,
temperature (0), context window (32,768), and compact diarized transcript
selection. Their Meeting Context V1 files identify the same meeting and four
shared confirmed participants; the speaker-aware context additionally includes
Jovana Husemann and the five confirmed mappings. Exact run IDs, hashes, flags,
and paths are in `manifest.json`.

The source runs were separate full-pipeline executions: their audio,
transcription, diarization, and exact protocol-input artifacts are
byte-identical, but the mapped-speaker run is not operational evidence that it
reused the anonymous-speaker run's Whisper or diarization artifacts. This case
therefore compares equivalent diarized input conditions with different speaker
identity mappings only.

## Evaluation role

The transcript is the evidentiary source. The human colleague protocol is
**not** a gold protocol: it is a human reference showing what an experienced
participant considered worth preserving in a concise working protocol. The two
Meeting Assistant files are comparison variants. This case must not force a
system to imitate the human protocol verbatim.

This is a valuable regression case because a long, five-speaker discussion
contains competing mental models, repeated disagreement and clarification, and
only partial convergence. It distinguishes strategic collection/evaluation and
coordination from QMS/process documentation and operational project work. It
also discusses responsibility repeatedly without always creating a concrete
personal commitment.

See `evaluation.md` for the qualitative comparison and explicit regression
expectations. No audio, raw model response, container log, or other unrelated
run artifacts are intentionally included.