# OTel 2.5 sample training rows (10 per category, for review before scaling)

Format follows `OTel_data_label.md`: one JSON object per line with `source_repo`, `source_id` (chunk id),
`source_spec_id: null`, `source_version: null`, and exactly two `messages` (user = document chunk + question,
assistant = answer grounded in that chunk).

| file | category | source collection | data origin |
|---|---|---|---|
| lte-signalling.jsonl | LTE S1AP/NAS control-plane events at the EPC | powder-lte-v1 | measured, POWDER real radios (srsRAN), benign vs fake-base-station runs |
| rf-signal-quality.jsonl | LTE measurement reports (RSRP/RSRQ/SINR/PHR/TA, neighbours) | powder-lte-v1 | measured, POWDER real radios |
| 5g-core-signalling.jsonl | NGAP, PFCP and SBI request flows in Open5GS | zmq-5g-core-sim-v1, powder-5g-core-sim-v1 | measured core signalling; radio simulated (ZMQ) or UERANSIM |
| ran-telemetry.jsonl | srsUE per-second radio metrics + ocudu gNB scheduler metrics | zmq-5g-core-sim-v1 | simulated radio link (ZMQ) |
| network-performance.jsonl | iperf3/ping/HTTP results and procedure timings per run | zmq-5g-core-sim-v1 | measured application results over a simulated link |
| faults-ground-truth.jsonl | injected NF restarts/stops/config caps (ground-truth timeline) with NF warning/error logs | zmq-5g-core-sim-v1, powder-5g-core-sim-v1 | measured logs, ground truth from orchestrator |
| aaa-radius.jsonl | FreeRADIUS Access/Accounting exchanges | powder-5g-core-sim-v1 | measured |

How they were made (`pipeline/trainrows/` in the collection repo):
1. `render.py` turns processed, pseudonymised records of one run into a plain-text document with a header
   (run id, scenario label, provenance statement, ground-truth timeline for fault runs) and a record table.
2. Documents are chunked on line boundaries (header repeated), sized so the largest row stays under ~3.5k tokens in the generator tokenizer (tables tokenize at ~2 chars per token), under the 4,096-token row limit.
3. `generate.py` asks `google/gemma-4-31b-it` (via OpenRouter) for one question an operations engineer would ask
   about that chunk and an answer using only the chunk; a second judge call checks every claim is supported and
   that provenance is not over-claimed (simulated radio never presented as over-the-air). Rejected rows are dropped.
4. Rows are scanned for raw identifiers (15-digit numbers, test IMSIs); subscriber ids are keyed pseudonyms.

Each `*.log.json` lists, per generated row, the run, scenario, judge verdict and token usage.
Rows are sampled round-robin over scenarios so each category covers several conditions.
Known limits of this first batch: questions are single-chunk and mostly descriptive (counts, sequences,
before/after comparisons); the judge model is the same model as the generator; chunks are tables, not prose.