---
# For reference on dataset card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/datasetcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/datasets-cards
pretty_name: Synthetic Multi-Turn Customer Support Tickets (Consumer Electronics)
language:
  - en
license: mit
task_categories:
  - text-generation
tags:
  - synthetic
  - customer-support
  - multi-turn
  - conversational
  - dialogue
size_categories:
  - 10K<n<100K
annotations_creators:
  - machine-generated
language_creators:
  - machine-generated
source_datasets:
  - original
configs:
  - config_name: default
    data_files:
      - split: train
        path: consumer-electronics-support.jsonl
---

# Dataset Card for Synthetic Multi-Turn Customer Support Tickets (Consumer Electronics)

99,930 wholly synthetic multi-turn customer-support conversations in a consumer-electronics
retail domain, each with assigned ticket metadata, a machine coherence score, and provenance
linking it to the run that produced it.

## Dataset Details

### Dataset Description

Every conversation is fabricated by a language model from a committed domain prompt. No real
support transcript, customer, or agent is involved at any point, and none was used as source
material. The conversations are between a customer of a mid-sized online retailer selling
consumer electronics — headphones, smart speakers, wearables, small home devices — and a support
agent employed by that retailer, covering billing, technical, account, shipping, product and
miscellaneous issues.

The dataset exists to support work that needs support-ticket-shaped text at scale — dialogue
modelling, routing and triage experiments, evaluation harnesses, pipeline load testing — without
the privacy exposure that real support archives carry.

Each record is one complete ticket: an ordered sequence of turns alternating customer and agent,
starting with the customer, plus the metadata the conversation was generated to fit.

Composition is **assigned before generation rather than measured after it**. Each record's
category, priority, channel, resolution status, turn count, subdomain and timestamps are seeded
choices made from the run seed and the record's position, so the distribution is a property of the
request rather than an accident of what the model happened to produce.

- **Curated by:** Robert Bates (arpieb)
- **Funded by [optional]:** Unfunded; personal project
- **Shared by [optional]:** Robert Bates (arpieb)
- **Language(s) (NLP):** en
- **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)

### Dataset Sources [optional]

- **Repository:** https://github.com/arpieb/ticket-dataset-generator
- **Paper [optional]:** None
- **Demo [optional]:** None

## Uses

### Direct Use

Suitable for:

- fine-tuning or evaluating multi-turn dialogue models on support-style exchanges — at ~100k
  records and ~100M characters of conversational content this is large enough to train a small
  model or adapt a larger one;
- ticket classification, routing, priority prediction and summarisation, where the label is known
  by construction rather than inferred — every record carries its category, priority, channel,
  resolution status and subdomain as assigned inputs, not as post-hoc annotation;
- stratified experiments: the 20 declared subdomains are balanced to within ~±2% of each other
  (4,890–5,121 records each), so per-subdomain slices are directly comparable;
- testing and benchmarking data pipelines that consume conversational JSONL, since every record is
  schema-validated and carries stable identifiers;
- teaching and demonstration, where a real support archive would be inappropriate to distribute.

Conversations are short by construction — 4 to 8 turns, mean 6.0, mean 1,003 characters of turn
content per record. The corpus is **not** suitable for work on long-horizon dialogue, multi-issue
tickets, or conversations that span sessions; none of those exist in it.

### Out-of-Scope Use

- **Not a sample of real support traffic.** Nothing here was observed. Frequencies, phrasing, and
  problem distributions reflect a prompt, a requested composition and a model, not any real
  customer population. The composition was *specified*, so it cannot be evidence of anything. Do
  not use it to estimate how often anything happens in the world.
- **Not a benchmark, and the coherence scores are not ground truth.** They were produced by the
  same model that wrote the conversations, against a rubric that has no calibration record (see
  Limitations). Treat `coherence_score` as a filtering artifact of this run.
- **Not a privacy-safe stand-in for de-identification research.** Synthetic-by-construction is not
  the same problem as de-identified-after-the-fact, and this dataset cannot be used to validate a
  de-identification method.
- **Not a basis for consequential decisions about real people.** Do not train systems that triage,
  prioritise or deny real customer requests on this corpus alone; the priority and resolution
  labels encode a requested distribution, not a policy anyone validated.

## Dataset Structure

One JSON object per line (JSONL, UTF-8). 99,930 records, 196,658,051 bytes. A single `train`
split; no test split is provided, because splitting is the consumer's decision and depends on the
task. Records are grouped by nothing in particular, so a random split is sound; a
subdomain-stratified split is straightforward from the `subdomain` field.

| Field | Type | Notes |
|---|---|---|
| `record_id` | string (UUIDv5) | Stable, derived from the run and position |
| `record_index` | int | Position in the run, ascending, 0–99,999 — **with 70 gaps, see below** |
| `run_id` | string (UUID) | The generation run; matches the manifest. One value across the corpus |
| `schema_version` | string | `1.0.0` |
| `source_id` | string | Domain prompt identity: `consumer-electronics-support.md@8d67eaf56b0a` |
| `subdomain` | string | Seeded choice from the prompt's declared list |
| `scenario` | string | The specific situation the model elaborated |
| `metadata` | object | `category`, `priority`, `channel`, `resolution_status`, `created_at`, `resolved_at` |
| `turns` | array | `{role: "customer"｜"agent", content: string}`, customer first, strictly alternating |
| `quality` | object | `coherence_score` (0–1), `rubric_id` |
| `generation` | object | `model_id`, `judge_model_id` |

Verified across all 99,930 records: every conversation starts with the customer and strictly
alternates roles, and `resolved_at` is present when and only when `resolution_status` is
`resolved`.

**`record_index` is not contiguous.** The run requested 100,000 records and wrote 99,930. Indices
run 0–99,999 with 70 positions absent. The manifest accounts for 55 of these as
`attempts_exhausted`; the remaining 15 are not accounted for by any recorded discard reason. Do
not use `record_index` as a row number or assume `max(record_index) + 1 == len(corpus)`.

**Achieved composition**, read from `composition_achieved` in the manifest and confirmed by
counting the corpus. Worst-member drift from the requested distribution is **0.022pp**
(`category.billing`, 24.9785% against 25%):

| Dimension | Distribution |
|---|---|
| category | technical 25.01% (24,992), billing 24.98% (24,961), account 20.01% (19,992), shipping 15.00% (14,991), product 10.00% (9,996), other 5.00% (4,998) |
| priority | normal 50.00% (49,967), low 20.00% (19,988), high 19.99% (19,979), urgent 10.00% (9,996) |
| channel | email 40.00% (39,970), chat 35.00% (34,980), phone 15.00% (14,987), web_form 10.00% (9,993) |
| resolution_status | resolved 70.02% (69,970), escalated 14.99% (14,981), unresolved 10.00% (9,993), abandoned 4.99% (4,986) |

(The `composition_drift_pp` block in the run report is miscomputed — it reports each member's drift
as the negative of its requested share, as though the achieved share were zero. The drift above
was recomputed from `composition_achieved` against `composition_requested`. See Limitations.)

Turn counts range 4–8, mean 6.006, near-uniform: 4 turns 19,725; 5 turns 20,013; 6 turns 20,009;
7 turns 20,328; 8 turns 19,855.

All 20 of the domain prompt's declared subdomains appear, between 4,890
(`technical-device-wont-power-on`) and 5,121 (`shipping-damaged-on-arrival`) records each.

`metadata.created_at` spans 2025-07-05T00:05:58Z to 2025-12-31T23:59:45Z, distributed across the
six months (14,960–17,330 records per month; July is short because the window opens on the 5th).

## Dataset Creation

### Curation Rationale

Real support tickets are among the densest sources of personal data that exist, which makes them
both valuable and largely undistributable. Scrubbing them after the fact is unreliable and
irreversible once published. This dataset takes the other route: admit no real identifiers at any
point, so there is nothing to scrub. Consumer-electronics support is a good domain for a first
release at scale — the issues are mundane, the stakes are low, and nothing in the domain invites
the model toward medical, financial or legal content that would carry risks a card like this one
could not discharge.

The generator is the deliverable; this corpus is one output of it. The pipeline enforces
reproducibility, provenance, and a blocking privacy scan as structural properties rather than
review-time checks.

### Source Data

#### Data Collection and Processing

Generated by `ollama_chat/gpt-oss:20b`, self-hosted, from a committed domain prompt document
(`consumer-electronics-support.md@8d67eaf56b0a`) declaring 20 subdomains.

Per record: the pipeline assigns metadata from the seed, prompts the model for a conversation
fitting that assignment, then applies four gates in order — structural validation, a blocking
privacy scan, coherence judging, and schema validation. A record failing any gate is discarded and
counted by reason; the slot is retried up to 3 times. Output is written to staging and moved to
the release path only after the privacy floor is demonstrated against known canaries.

**This run:** 105,236 generated, 99,930 written — a 5.04% discard rate. Discards by reason:
2,144 `coherence_below_threshold`, 1,776 `structural_invalid`, 761 `privacy_finding`, 552
`turn_count_out_of_range`, 55 `attempts_exhausted`, 18 `unjudgeable` (5,306 total, which
reconciles exactly with generated minus written). Zero duplicates. Zero records reached the output
with a blocking privacy finding. The run was resumed 3 times and its recorded outcome is
`completed`, verdict `pass`.

Reproduction inputs: run `8c56469b-f094-4686-90dc-29715bab4228`, seed `42`, code revision
`ab247fa42a66`, corpus SHA-256
`a49bcc9ca0d228d456ae6bd371e8517940629ecd9e97140d65c45cd8c56c755b` (verified against the file).

**The code revision was dirty.** `code_revision.dirty` is `true` in the manifest: this corpus was
generated from a working tree with uncommitted changes, so it cannot be reproduced from any
commit. `ab247fa42a66` names the nearest ancestor commit, not the code that ran.

Reproduction is in any case **structural, not textual**. No model sampling seed was set
(`models.generator.sampling_seed` is `null`), so a rerun with the same seed, config, prompt and
rubric lands every record on the same composition, subdomain, turn count and timestamps, but the
conversation text differs on every run. This is a property of the method, not a defect of the run.

The run's resolved configuration is recorded in the manifest and is **not** the repository's
`configs/samples/release.toml`: it used `turns.max = 8` (not 12), `time_window.start = 2025-07-05`
(not 2025-07-01), `max_concurrency = 4`, `checkpoint_interval = 100`, and no budget ceiling. Use
`config-from-manifest` rather than the sample config to recover what actually ran.

#### Who are the source data producers?

A language model, prompted by an automated pipeline. No humans produced any conversational
content, and no human-authored text was used as source material.

### Annotations [optional]

#### Annotation process

Each conversation is scored 0–1 for coherence by a second model call against a committed, versioned
rubric (`coherence-v2`) with four weighted criteria: `single_issue` (0.30), `role_consistency`
(0.25), `conversational_flow` (0.25), `metadata_fit` (0.20). The record's score is the weighted
mean. Records below 0.8 are discarded before release; the score of every surviving record is
retained. 2,144 records were discarded on this gate.

Score distribution (computed from the corpus — the manifest's
`coherence_score_distribution` field was not populated, see Limitations): mean 0.960, median 1.0,
min 0.80, max 1.0, standard deviation 0.053, across 211 distinct values. By bucket —
0.80–0.85: 2,886 (2.89%); 0.85–0.90: 3,952 (3.95%); 0.90–0.95: 28,049 (28.07%); 0.95–1.00: 6,079
(6.08%); **exactly 1.00: 58,964 (59.01%)**. A further 26,018 records (26.04%) sit at exactly 0.90.

#### Who are the annotators?

`ollama_chat/gpt-oss:20b` — **the same model, at the same version, that generated the
conversations.** See Limitations.

**No human calibration exists for this rubric.** `calibration/` contains only its README; there is
no `coherence-v2.calibration.json` and no record of any reviewer having read any sample of this
corpus. The judge behind every score in this dataset is uncalibrated. The repository documents
this as an accepted deferral (checklist item CHK063), not as an oversight — but a consumer of the
scores should treat them accordingly.

#### Personal and Sensitive Information

The dataset contains no real personal data by construction. Identifier-shaped values are drawn
from ranges reserved for fiction — RFC 2606 domains (`example.com`, `.test`, `.invalid`), NANP
`555-0100`–`555-0199` phone numbers, published payment-card test numbers — so they cannot refer to
anyone. The domain prompt makes this the primary control and forbids bare runs of nine or more
digits, so that invented order and serial numbers cannot collide with government identifier or
phone number shapes.

Every record was scanned before admission by the `datafog-regex` detector over `turns[].content`
and `scenario`, covering `CREDIT_CARD`, `EMAIL`, `IP_ADDRESS`, `PHONE` and `US_SSN`. **761 records
were blocked and discarded on this gate**, and zero records with a blocking finding reached the
output. Nothing was quarantined. No privacy exception has ever been approved for this project;
`privacy/exceptions.json` does not exist.

The run report's per-record privacy counters are unusable: `records_examined` and
`fields_examined` are both `0` and `findings` is empty, which contradicts the 761 recorded privacy
discards. The gate demonstrably ran and blocked; the accounting of what it examined did not
survive into the report. This card therefore cannot state how many fields were scanned or how many
findings were exempt by reserved range. See Limitations.

The scan does not detect: non-US government identifiers, postal codes, full postal addresses, IBAN
bank account numbers, or person names. These are declared gaps, restated here so a clean scan is
not mistaken for coverage it does not provide. Person names in particular are present throughout
and are fabricated.

## Bias, Risks, and Limitations

**The judge shares a model with the generator.** `ollama_chat/gpt-oss:20b` wrote and scored every
one of the 99,930 conversations. A model evaluating its own output is a known source of inflated
agreement, and the score distribution is consistent with that.

**The judge did not use the scale.** Rubric v2 exists precisely because v1 collapsed to three
values, and it instructs the judge to reserve 1.0 for "no observable flaw of this kind". 59.01% of
records scored exactly 1.00 and a further 26.04% exactly 0.90 — 85% of the corpus on two values,
and only 211 distinct values across ~100k records. The rubric's own stated failure mode has
recurred in weaker form. The scores separate the bottom few percent from everything else and
should not be read as a quality ordering within the corpus.

**Nobody has checked the judge.** There is no calibration record for `coherence-v2`. Every claim
this corpus makes about quality rests on one uncalibrated model scoring its own output. This is
the single largest gap in the dataset's provenance.

**The provenance record has holes.** Beyond the dirty code revision, several manifest and report
fields are empty or wrong: `coherence_score_distribution` is `{"_count": 0}`;
`privacy.records_examined` and `fields_examined` are `0` with an empty `findings` list despite 761
privacy discards; `composition_drift_pp` reports each member's drift as the negative of its
requested share; `budget.spent_model_calls` is `0` and `budget.spent_seconds` is `2.071` for a run
that made on the order of 200,000 model calls; and the single recorded segment spans 4.6 seconds
with `first_record_index` (100000) greater than `last_record_index` (99929). The corpus itself is
internally consistent and its hash verifies, and the discard counts reconcile exactly with
generated-minus-written — but a reader should not trust the manifest's timing, budget, coherence
or privacy-volume counters, and every number in this card that could be recomputed from the
records was recomputed rather than quoted.

**Scenario repetition.** 98,239 distinct `scenario` strings cover 99,930 records: 1,002 scenario
strings are used more than once, affecting 2,693 records (2.7%). The repeats cluster on a few
attractors — "Customer received a Bluetooth speaker instead of the ordered noise-cancelling
headphones" and variants appear dozens of times across `shipping-wrong-item-received`, and
duplicate-charge-on-a-smart-speaker dominates `billing-duplicate-charge`. Exact-duplicate
detection found zero duplicate *records*, so the conversations differ; the situations behind them
are narrower than the scenario count suggests.

**Single domain, single model, single prompt, single seed, single run.** One
consumer-electronics prompt, one 20B model, seed 42, one run. Phrasing, product names, escalation
language and problem framing will be markedly less varied than real traffic, and stylistic tics of
one model run through the entire corpus. The size of the corpus does not mitigate this: 99,930
records from one model are not 99,930 independent samples of anything.

**Short conversations only.** 4–8 turns by configuration. Real support threads that reopen, span
channels, or involve a second agent have no analogue here.

**Composition is prescribed, not observed.** Every distribution in this card was requested. That
70% of tickets are `resolved` and 10% are `urgent` says what the config asked for and nothing
about consumer electronics support.

**Timestamps are seeded, not causal.** `created_at` and `resolved_at` are drawn from the run
window and a duration range; they do not encode business hours, weekday effects, seasonality, or
any relationship between priority and resolution latency. Do not use this corpus for queueing or
SLA modelling.

**Fabricated products and companies.** Product names appearing in conversations are invented. Any
resemblance to real products is incidental and not an endorsement or a description.

**Privacy coverage is narrower than "scanned" implies.** Five entity types, two fields, one
regex-based detector. Person names, addresses, postal codes, IBANs and non-US identifiers are
outside it by declaration.

### Recommendations

Users should be made aware of the risks, biases and limitations of the dataset. Specifically:

- **Do not filter or rank on `coherence_score`.** Everything in the corpus already passed 0.8, and
  85% of it sits on two values from an uncalibrated judge scoring its own work. If you need a
  quality signal, score the conversations yourself with a different model, or draw a sample and
  read it.
- **Do not treat any distribution as descriptive.** Composition, turn counts, subdomain balance
  and timestamps were all specified before generation. Use them as controlled variables, which is
  what they are good for, not as observations.
- **Deduplicate on `scenario` if scenario diversity matters** to your task — dropping to one
  record per scenario string costs 1,691 records and removes the attractor clusters.
- **Do not index on `record_index`.** It has 70 gaps.
- **Do not rely on the privacy scan for entity types it declares out of scope.** If your use
  requires name- or address-level assurance, run your own detector before use.
- **Expect one model's voice.** For anything where stylistic diversity matters, regenerate from
  the repository across several models, prompts and seeds and pool the results rather than
  resampling this corpus.
- **Do not attempt textual reproduction.** No sampling seed was set and the tree was dirty; rerun
  from the manifest to reproduce the *structure*, and expect different conversations.

## Citation [optional]

**BibTeX:**

```bibtex
@misc{bates_synthetic_support_tickets_2026,
  author       = {Bates, Robert},
  title        = {Synthetic Multi-Turn Customer Support Tickets (Consumer Electronics)},
  year         = {2026},
  version      = {1.0.0},
  howpublished = {\url{https://github.com/arpieb/ticket-dataset-generator}}
}
```

**APA:**

Bates, R. (2026). *Synthetic Multi-Turn Customer Support Tickets (Consumer Electronics)*
(Version 1.0.0) [Data set]. https://github.com/arpieb/ticket-dataset-generator

## Glossary [optional]

- **Slot** — one unit of generation work. Its metadata is assigned before any model call, which is
  what makes the composition exact and lets a discarded record be retried without changing the
  corpus shape.
- **Subdomain** — a category declared by the domain prompt document (e.g.
  `billing-invoice-discrepancy`). Chosen by seed before dispatch; the model elaborates a specific
  scenario within it.
- **Scenario** — the specific situation the model invented within its assigned subdomain, stored
  on the record as free text. Not drawn from a fixed list, which is why it repeats only rarely.
- **Exempt by range** — a privacy finding whose value comes from a range a standard reserves for
  fiction. Reported rather than hidden, so the scan never looks cleaner than it was.
- **Structural reproduction** — same seeded choices at every position, different conversation text.
  What this pipeline guarantees when no model sampling seed is set.
- **Blocking finding** — a privacy detection that is not exempt by reserved range. It discards the
  record; it never appears in the corpus.

## More Information [optional]

The manifest (`8c56469b-f094-4686-90dc-29715bab4228.manifest.json`) and run report
(`…report.json`) for this corpus are the authoritative provenance record: they carry the full
resolved configuration, input hashes, code revision, and the complete filter accounting — subject
to the counter defects noted in Limitations.
`ticket-dataset-generator config-from-manifest` recovers the exact configuration from the manifest, and
`ticket-dataset-generator generate --from-manifest` reproduces the run, refusing if any recorded input has
since changed. Given the dirty code revision, a `--from-manifest` rerun reproduces the structure
under whatever code is checked out, not under the code that produced this corpus.

## Dataset Card Authors [optional]

Robert Bates (arpieb), with drafting assistance from Claude Code. Every figure was read from the
run manifest, the run report, the domain prompt, the rubric, or recomputed from the corpus itself.

## Dataset Card Contact

arpieb via the GitHub repository above.