Run: 2026-09-23-01M36RAY3BAK4ANZGCBEYX5EQV

Run Details

Duration:
1m 25s
Cost:
$0.002204
Provider:
OpenAI

Model input

System
You are an expert fiction writer. Always keep the following rules in mind: - Write in past tense and use General English spelling, grammar, and colloquialisms/slang. - Write in active voice - Always follow the "show, don't tell" principle. - Avoid adverbs and cliches and overused/commonly used phrases. Aim for fresh and original descriptions. - Convey events and story through dialogue. - Mix short, punchy sentences with long, descriptive ones. Drop fill words to add variety. - Skip "he/she said said" dialogue tags and convey people's actions or face expressions through their speech - Avoid mushy dialog and descriptions, have dialogue always continue the action, never stall or add unnecessary fluff. Vary the descriptions to not repeat yourself. - Put dialogue on its own paragraph to separate scene and action. - Reduce indicators of uncertainty like "trying" or "maybe" When writing text: - NEVER conclude the scene on your own, follow the beat instructions very closely. - NEVER end with foreshadowing. - NEVER write further than what I prompt you with. - AVOID imagining possible endings, NEVER deviate from the instructions. - STOP EARLY if the continuation contains what was required in the instructions. You do not need to fill out the full amount of words possible.
User/Human
Take into account the following glossary of characters/locations/items/lore... when writing your response: <codex> <character id="4416" name="Detective Harlow Quinn" aliases="Quinn, Detective Quinn, Harlow" pronouns="she/her" species="Human" storyRole="Antagonist"> <eyeColor>brown</eyeColor> <hair>closely cropped salt-and-pepper</hair> <height>5'9"</height> <age>41</age> <jawline>sharp jaw</jawline> <watch>worn leather watch on her left wrist</watch> <bearing>military precision</bearing> <description>A Metropolitan Police detective with 18 years of decorated service who suspects the clique is involved in criminal activity.</description> <backstory>She lost her partner, DS Morris, during a case three years ago under unexplained circumstances that had supernatural origins she doesn't yet understand.</backstory> </character> <character id="4417" name="Tomás Herrera" aliases="Tommy, Tomás, Herrera" pronouns="he/him" species="Human" storyRole="Support"> <eyeColor>warm brown</eyeColor> <hair>short curly dark brown</hair> <height>5'10"</height> <age>29</age> <skinTone>olive skin</skinTone> <scar>scar running along his left forearm from a knife attack</scar> <medallion>Saint Christopher medallion around his neck</medallion> <birthplace>Seville, Spain</birthplace> <description>A former paramedic who provides off-the-books medical care for the clique.</description> <backstory>Born in Seville, Spain. He moved to London to work for the NHS but lost his license after administering unauthorized treatments to supernatural patients.</backstory> </character> <location id="854" name="The Raven's Nest" aliases="the Nest, Raven, Silas' bar" realm="Earth"> <signColor>distinctive green neon sign above the entrance</signColor> <secretRoom>hidden back room accessible through a bookshelf, used for clandestine meetings</secretRoom> <description>A dimly lit bar in Soho, London. The walls are covered with old maps and black-and-white photographs.</description> </location> <location id="856" name="The Veil Market" aliases="Veil Market, the Market, underground market" realm="Earth"> <currentLocation>abandoned Tube station beneath Camden</currentLocation> <entryRequirement>bone token</entryRequirement> <schedule>moves locations every full moon</schedule> <description>A hidden supernatural black market that sells enchanted goods, banned alchemical substances, and information.</description> </location> </codex> Continue the story using the following instructions: <instructions> Write a fully fleshed out scene of at least 1000 words in the thriller genre. A detective is pursuing a suspect through rain-soaked city streets at night. The chase takes them from the street level down into an underground market. The detective must decide whether to follow the suspect into unfamiliar and potentially dangerous territory. Use the codex entries above as your world reference for characters and setting. Only include the prose of the scene without any markdown or commentary. </instructions>

Model response

AI
Rafe Bell glanced back at the Camden High Street traffic and saw Harlow Quinn coming through it. He broke into a run. Quinn shoved past a man wrestling an umbrella into shape and cut between two taxis. Horns blared behind her. Bell slipped across the wet road, his black jacket flashing between headlights, then ducked into a narrow lane beside a shuttered off-licence. “Rafe Bell! Stop!” He looked over his shoulder. His heel struck a puddle. He caught himself on a brick wall and kept going. Quinn reached the alley mouth as his coat vanished around the bend. She put her hand to her radio. “Control, Detective Quinn. Foot pursuit, Camden High Street, suspect heading north through a service lane. Rafe Bell, black jacket, grey trousers.” The speaker crackled against her palm. “Received. Units are tied up. Keep us updated.” Bell vaulted a stack of milk crates. One toppled behind him. Quinn stepped over it, her shoes sliding on the slick paving. Rain ran from her cropped salt-and-pepper hair into her collar. She wiped it from her eyes with the back of her hand and picked up speed. She had followed Bell from Soho, keeping two cars and a bus between them. He had left the Raven’s Nest with an envelope under his jacket and no hurry in his step. The green neon sign had washed his face in colour as he crossed the street. Quinn had watched from a cab, then lost him for three minutes when traffic clogged Tottenham Court Road. Now he was running as if the envelope had caught fire. The alley opened onto a side street. Bell ran past a closed bakery and shouldered through a knot of people spilling out of a pub. Quinn raised her badge. “Police. Clear the pavement.” A woman dragged her companion aside. Bell used the gap to turn down another street, heading away from the shops and towards a row of boarded buildings. Quinn’s watch pulled at her wrist as she ran; the leather strap had darkened with rain. She kept her left arm close to her body and drove her right hand forward. Bell reached a junction and cut between two parked vans. Quinn followed. The street narrowed, then dipped towards a low brick arch, its old station lettering buried beneath layers of paint and pasted bills. A chain-link fence blocked the entrance. Someone had cut a person-sized hole through it. Bell slipped through. Quinn caught the fence with one hand. Its wet metal bit into her palm. She dragged herself through the opening and landed in a service yard scattered with rubbish bags and broken pallets. “Rafe!” He glanced back from the far side of the yard. For one second, he looked less frightened than angry. “You should’ve stayed at the bar, Detective.” “Then you shouldn’t have run.” He turned and disappeared down a flight of stone steps. Quinn reached the top. The stairs dropped beneath the arch, past a faded blue tile that read CAMDEN CROWN. She knew no station by that name. A corrugated shutter covered the lower landing. Bell stood at it with his shoulder turned, one hand pressed to the bricks beside the frame. The surface beneath his palm shifted. Quinn stopped at the stairhead. Bell pulled something from his pocket: a pale disc, no larger than a coin. He pressed it to a tarnished brass plate set into the wall. The shutter lifted without a sound. He darted under it. The opening narrowed. Bell’s hand came back into view, and the pale disc skittered across the stair tread. The shutter struck the floor. Quinn descended three steps at a time. The disc spun once, wobbled, then settled against the wall. She scooped it up. Bone, cut into a flat token with a dark line carved through the centre. The shutter had no handle. The brass plate sat flush in the brickwork. “Control, Quinn. I’m at a disused station entrance beneath Camden Crown. Suspect entered an underground space. Send units to my last position.” Only static answered. She moved to the shutter and pressed the token to the plate. Cold crept through the bone and into her fingers. A heavy mechanism clicked behind the bricks. The shutter rose a handspan, paused, then climbed high enough for her to crouch beneath it. Quinn held at the threshold. The stairs continued below. A strip of amber light cut across the bottom step, and voices rose through it: bargaining, laughter, a child crying. A smell of wet stone, hot oil and something sharp enough to sting her nose drifted up the stairwell. She could back out. Close the entrance. Mark the location and bring a team. Bell had gone into an unknown space beneath a station that did not exist on any map she knew. The token in her hand had opened a door with no lock. Her radio had lost Control. Every sensible line in her training led back up the stairs. At the bottom, Bell’s voice cut through the crowd. “Move. I’m not paying twice.” Quinn heard a woman answer him. Then came the scrape of hurried feet. She looked at the token. The carved line held a dark stain in its groove. She slipped it into her coat pocket, drew her torch, and descended. The passage opened onto an abandoned platform turned into a market. Stalls occupied the old track bed beneath a canopy of tangled cables. Canvas awnings covered tables crowded with glass vials, brass instruments, knives wrapped in cloth and packets of dried leaves. Lanterns hung from the tiled pillars. Their light caught faces that turned as Quinn stepped into view. A man with silver rings across his brow stopped arranging a tray of black seeds. At the next stall, a woman in a red headscarf slid a wooden box beneath the table. No one looked surprised to see Quinn. They looked annoyed. Bell pushed between them, shoulder-checking a vendor with a rack of copper charms. “Police!” Quinn called. “Rafe Bell, stop where you are.” The market went quiet in pieces. One conversation broke off, then another. Bell turned at the far end of the platform. He held the envelope in one fist. A broad man in a patched coat stepped into Quinn’s path. “Market business,” he told her. “Take it outside.” “Move.” His eyes dropped to the token’s pale shape beneath the cloth of her coat. “You came through the front.” “I came through a door.” “That door wasn’t for you.” Quinn showed her badge, kept the torch trained on Bell, and edged around him. “Then you can explain it to the uniformed officers when they arrive.” The man gave a dry laugh. “Your radio won’t reach them down here.” Quinn pressed the transmit button. Static hissed. Bell used the pause to slip behind a row of stalls. She pushed past the man in the patched coat. A woman caught at Quinn’s sleeve; Quinn tore free without breaking stride. Bell shoved through a bead curtain into a narrow aisle between two stone pillars. “Rafe, hands where I can see them!” “Not in this place.” “Your choice.” He ran again. Quinn set off after him, torch beam cutting across the crowded aisle. A stallholder yanked a hanging rack aside. Its metal hooks clanged against her shoulder. She kept her feet, ducked beneath a low awning and saw Bell ahead, forcing his way through the market while traders shouted after him. “Stop him!” someone yelled. “No police here!” Quinn closed the gap, one hand reaching for the cuffs at her belt. “Rafe! You’re under arrest.”
created_at"2026-09-23T09:07:51.555Z" 9/23/2026, 9:07:51 AM
model"openai/gpt-6-luna-20260922"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency70184
moderation_latency(null)
generation_time84927
tokens_prompt1104
tokens_completion1889
native_tokens_prompt984
native_tokens_completion4212
native_tokens_completion_images0
native_tokens_reasoning2639
native_tokens_cached0
num_media_prompt(null)
num_input_audio_prompt(null)
num_media_completion0
num_search_results(null)
num_fetches(null)
web_search_engine(null)
origin"https://ncbench.com/"
is_byokfalse
finish_reason"stop"
native_finish_reason"completed"
service_tier"default"
usage0.0022044
router(null)
user_agent"langchainjs-openai/1.0.0 ((node/v24.21.0; linux; x64))"
http_referer(null)
request_id"req-1790154471-aeephh0e1UCb2kTg7dJX"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1790154471-IsBsxSVBv7Y6r9n47CCX"
upstream_id"resp_0877c4991ba7b2ef016ab396e7b2e487d1a9c7fb6fece21fbf"
provider_responses
0
endpoint_id"05e94e02-b9c2-4bbb-ba55-4082ee9ad687"
id"resp_0877c4991ba7b2ef016ab396e7b2e487d1a9c7fb6fece21fbf"
is_byokfalse
latency511
model_permaslug"openai/gpt-6-luna-20260922"
provider_name"OpenAI"
status200
total_cost0.0022044
cache_discount(null)
upstream_inference_cost0
provider_name"OpenAI"
response_cache_source_id(null)
data_region"global"
workspace_id"97e315e5-d303-487d-83c1-83180e8a13d4"

Evaluation details

Result Evaluator Details Meta Data
100.00% Adverbs in dialogue tags
Target: ≤10% dialogue tags with adverbs
totalTags3
adverbTagCount0
adverbTags(empty)
dialogueSentences25
tagDensity0.12
leniency0.24
rawRatio0
effectiveRatio0
100.00% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount1263
totalAiIsmAdverbs0
found(empty)
highlights(empty)
100.00% AI-ism character names
Target: 0 AI-default names (17 tracked, −20% each)
codexExemptions(empty)
found(empty)
100.00% AI-ism location names
Target: 0 AI-default location names (33 tracked, −20% each)
codexExemptions(empty)
found(empty)
100.00% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount1263
totalAiIsms0
found(empty)
highlights(empty)
100.00% Cliché density
Target: ≤1 cliche(s) per 800-word window
totalCliches0
maxInWindow0
found(empty)
highlights(empty)
100.00% Emotion telling (show vs. tell)
Target: ≤3% sentences with emotion telling
emotionTells1
narrationSentences115
matches
0"looked surprised"
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount1
hedgeCount0
narrationSentences115
filterMatches
0"watch"
hedgeMatches(empty)
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences137
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen24
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords1263
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions2
unquotedAttributions0
matches(empty)
16.67% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions54
wordCount1118
uniqueNames13
maxNameDensity2.15
worstName"Quinn"
maxWindowNameDensity4.5
worstWindowName"Quinn"
discoveredNames
Bell19
Camden1
High1
Street1
Harlow1
Quinn24
Soho1
Raven1
Nest1
Tottenham1
Court1
Road1
Control1
persons
0"Bell"
1"Harlow"
2"Quinn"
3"Raven"
places
0"Camden"
1"High"
2"Street"
3"Soho"
4"Tottenham"
5"Court"
6"Road"
globalScore0.427
windowScore0.167
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences86
glossingSentenceCount0
matches(empty)
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount1263
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences137
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs66
mean19.14
std18.14
cv0.948
sampleLengths
017
15
241
33
420
519
621
76
88
948
1076
1129
124
1358
1448
153
1633
171
1819
197
205
2110
2250
236
245
2532
264
2723
2835
2913
3022
313
3244
335
3443
3561
369
375
3813
3927
4059
4142
4213
439
4428
4511
468
471
4814
495
100.00% Passive voice overuse
Target: ≤2% passive sentences
passiveCount0
totalSentences115
matches(empty)
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount1
totalVerbs190
matches
0"was running"
100.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount0
semicolonCount2
flaggedSentences2
totalSentences137
ratio0.015
matches
0"Quinn’s watch pulled at her wrist as she ran; the leather strap had darkened with rain."
1"A woman caught at Quinn’s sleeve; Quinn tore free without breaking stride."
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount1124
adjectiveStacks0
stackExamples(empty)
adverbCount21
adverbRatio0.018683274021352312
lyAdverbCount1
lyAdverbRatio0.0008896797153024911
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences137
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences137
mean9.22
std5.4
cv0.586
sampleLengths
017
15
215
34
422
53
65
75
810
912
107
1121
126
138
147
154
1611
1710
1816
1914
2018
2115
2218
2311
247
2518
264
274
286
2921
3016
3115
3210
332
3422
356
368
373
387
397
4019
411
4210
439
447
455
4610
474
4815
497
50.12% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats3
diversityRatio0.31386861313868614
totalSentences137
uniqueOpeners43
60.06% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount2
totalSentences111
matches
0"Only static answered."
1"Then came the scrape of"
ratio0.018
100.00% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount31
totalSentences111
matches
0"He broke into a run."
1"He looked over his shoulder."
2"His heel struck a puddle."
3"He caught himself on a"
4"She put her hand to"
5"She wiped it from her"
6"She had followed Bell from"
7"He had left the Raven’s"
8"She kept her left arm"
9"Its wet metal bit into"
10"She dragged herself through the"
11"He glanced back from the"
12"He turned and disappeared down"
13"She knew no station by"
14"He pressed it to a"
15"He darted under it."
16"She scooped it up."
17"She moved to the shutter"
18"She could back out."
19"Her radio had lost Control."
ratio0.279
18.56% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount98
totalSentences111
matches
0"Rafe Bell glanced back at"
1"He broke into a run."
2"Quinn shoved past a man"
3"Horns blared behind her."
4"Bell slipped across the wet"
5"He looked over his shoulder."
6"His heel struck a puddle."
7"He caught himself on a"
8"Quinn reached the alley mouth"
9"She put her hand to"
10"The speaker crackled against her"
11"Bell vaulted a stack of"
12"Quinn stepped over it, her"
13"Rain ran from her cropped"
14"She wiped it from her"
15"She had followed Bell from"
16"He had left the Raven’s"
17"The green neon sign had"
18"Quinn had watched from a"
19"The alley opened onto a"
ratio0.883
45.05% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount1
totalSentences111
matches
0"Now he was running as"
ratio0.009
100.00% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences54
technicalSentenceCount1
matches
0"Their light caught faces that turned as Quinn stepped into view."
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags3
uselessAdditionCount0
matches(empty)
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags3
fancyCount1
fancyTags
0"someone yelled (yell)"
dialogueSentences25
tagDensity0.12
leniency0.24
rawRatio0.333
effectiveRatio0.08
89.6817%