Run: 2026-09-23-01M37HMQEFGJDT3QGE0X08EAMR

Run Details

Duration:
51.8s
Cost:
$0.025898
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="4414" name="Eva Kowalski" aliases="Eva, Evie" pronouns="she/her" species="Human" storyRole="Best Friend"> <eyeColor>green</eyeColor> <hair>curly red</hair> <height>5'4"</height> <age>26</age> <glasses>round glasses</glasses> <satchel>worn leather satchel full of books</satchel> <freckles>freckled complexion</freckles> <nervousHabit>tucking hair behind her left ear</nervousHabit> <description>Aurora's childhood friend and an occult researcher.</description> <backstory>Moved to London two years before Aurora and works as a research assistant at the British Museum's restricted archives. She holds a master's degree in Ancient History from Oxford.</backstory> </character> <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> <item id="2004" name="Veil Compass" aliases="the Compass, shadow compass" type="Tool" origin="The Veil Market"> <casingMaterial>small brass compass</casingMaterial> <faceMarkings>face etched with protective sigils</faceMarkings> <needle>points toward the nearest supernatural rift or portal</needle> <patina>casing has a patina of verdigris</patina> <creator>crafted by a Shade artisan</creator> <description>A compass attuned to supernatural energy.</description> </item> </codex> Continue the story using the following instructions: <instructions> Write a fully fleshed out scene of at least 1000 words in the mystery genre. A detective arrives at a crime scene in an unusual location. Something is wrong — the evidence doesn't add up. A colleague is present, offering a different interpretation. Through careful observation and deduction, the detective begins to see what others have missed. 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
Detective Harlow Quinn reached the bottom of the service stairs and found a ticket barrier bolted across the passage. No trains had used the station beneath Camden for decades. Someone had fitted the barrier with a new steel latch. A constable stood beside it, holding a plastic evidence bag. “Found this in the lock when we came down.” Inside lay a bone disc the size of a ten-pence piece. A hole ran through its centre, and tiny grooves circled one face. “Who opened it?” “I did. Gloves on.” Quinn checked the latch, then stepped through. The constable stayed at his post. Beyond him, electric lanterns lit the platform in hard white patches. Canvas awnings crowded the old tracks. Under them stood tables, crates and empty display racks. Someone had cleared most of the goods but left behind a smell that caught in Quinn’s throat: lamp oil, wet stone, crushed herbs and the copper tang of blood. Her colleague crouched beside the body near the platform edge. Detective Sergeant Nikhil Patel looked up over a strip of scene tape. “Morning, Harlow. Mind the chalk.” Quinn stopped short of a pale circle drawn on the tiles. The victim lay across it, his legs outside the ring and his head within. He wore a dark coat with a row of small inner pockets. Blood had dried beneath his right ear. “What have we got?” “Male, fifty or thereabouts. No identification. Porter found the gate open at six and called it in. Uniforms came down expecting squatters and found him.” Patel stood and pulled off one glove. “Looks like a market for stolen goods. Something went wrong during a deal.” Quinn looked along the platform. Hand-painted signs hung above the abandoned stalls. One read MEMORY REPAIRS. Another offered WINTER APPLES, though no apples remained. A price card beside a stack of empty velvet trays listed six crowns. “Stolen from where?” Patel followed her gaze. “Half this stuff looks like theatre junk. The rest has gone.” A forensic photographer moved around the body. Quinn waited for the flash, then crossed to a stall beside the tiled wall. Dust covered its counter except for six clean squares where boxes had rested. Beneath it sat an abandoned mug. A thin skin had formed over the tea. “They left in a hurry,” Patel called. “Not everyone.” Quinn pointed at the mug. A dark lipstick mark stained its rim. Two stalls down, a small electric kettle still held warm water. Someone had drunk tea after the man on the floor had stopped bleeding. Patel joined her. “Or before he died.” “The porter called at six. What time did he find the gate open?” “Just after. He noticed the latch when he checked the upper passage.” Quinn glanced at the barrier behind them. The constable’s boots showed through its bars. “And the pathologist?” “On his way. Blood feels dry. We’re waiting for him before we put a time on it.” She bent towards the mug without touching it. A crescent of clean wood marked where its handle had rested before someone moved it closer to the edge. A loose thread of red wool clung to a splinter in the counter. “Who moved the cup?” Patel leaned in. “Forensics haven’t reached this stall.” Quinn turned towards the body. Three officers had crossed the platform to get there; their blue overshoes had left faint prints in the dust. She could pick out the porter’s broad work boots near the barrier. None came within a metre of the mug. “Get it photographed before someone decides they need a drink.” Patel waved the photographer over. Quinn walked to the chalk circle. Up close, she saw that someone had drawn it with a single steady stroke, interrupted only where the victim’s coat covered the tiles. The man’s left hand lay palm-up. A green stain crossed the creases of his fingers. His right hand gripped a small brass compass. Patel caught her looking. “A souvenir, most likely. There’s a stall full of them near the stairs.” “This one was in his hand?” “Closed around it. We haven’t shifted him.” Quinn crouched. Verdigris crusted the compass hinge, and tiny marks covered its face. The needle pointed across the platform towards an old advertising recess. The sign inside promoted a summer rail excursion, its smiling family faded almost to ghosts. She checked her worn leather watch. North lay back towards the stairs, give or take the curve of the tunnel. She turned her head to judge the direction again. “Steel down here,” Patel offered. “Rails, wiring, half a century of rubbish.” “Mm.” The victim’s fingernails held grit and a thin strip of blue paper. Blood filled the seams of his coat collar, but little marked his chest. Quinn followed the stain beneath his head. It ran across two tiles, stopped at the chalk line and gathered in the groove beside it. “Did someone draw the circle after the blood spread?” “That’s what it looks like.” “No.” She pointed. “The chalk crosses the groove. The blood stopped before it reached it.” Patel crouched beside her. “A raised edge?” “Feel it.” He pressed a gloved fingertip against the tile. “Nothing.” “Blood on the collar, under the head. None on the upper wall, none on the stall behind him. He fell here, or someone put him here before much came out.” Quinn looked at the chalk on either side of the body. “The person with the chalk stood close enough to reach round him. Where did they put their feet?” Patel studied the floor. “We’ve got prints everywhere.” “Near his head.” He moved his torch across the tiles. Dust showed in the joints, but the space behind the dead man’s shoulders remained clean. A long drag mark ran from the body towards the platform edge. “There,” Patel said. “They pulled him from the tracks.” Quinn followed the mark. It ended at the lip of the platform. Below, old rails disappeared into a black tunnel. A ladder rose from the track bed, and a film of dust lay across every rung. “Not from down there.” She pointed to the ladder. “No marks on it. And that line’s too narrow for his shoulders.” “A crate, then. They moved the stock and dumped him.” “Perhaps.” Patel looked at her. “You haven’t liked a single answer since you got here.” Quinn rose. “I like answers that fit.” She followed the drag mark away from the body. Its edges stayed straight for three metres, then broke where several heel prints cut across it. The line didn’t lead to the barrier. It led towards the advertising recess. A second mark ran beside it, so faint she saw it only when Patel’s torch struck the tiles at an angle. Two narrow tracks, the width of a handcart’s wheels. She pictured a loaded trolley moving across the platform. At the recess, both tracks stopped. “There’s another service door,” Patel said. “Behind the poster, most likely. That’s how they cleared the merchandise.” Quinn faced the recess. The advertisement sat inside a frame of cracked cream tiles. No hinges. No handle. Mortar filled every joint. She pressed her knuckles against the centre and heard a dull, solid knock. “The builders sealed it,” Patel said. “They might have fitted a panel over the front.” “Get someone to check the plans.” “I already asked. Records are pulling them.” The photographer finished at the stall and approached the body. Quinn stepped aside. As he lifted his camera, its strap brushed a brass hook fixed to an awning pole. The hook swung, casting a thin shadow across the floor. Quinn looked at the shadows beneath the other stalls. Every awning along the platform faced the tracks, ready for customers walking between counter and edge. The three nearest the advertisement faced the wall. “Who set those up?” Patel lowered his torch. “What?” “The stalls. The sellers stood with their backs to the tracks.” “People could come through both ends.” “Then where are the marks by the barrier?” Patel looked down the length of the platform. Dust gathered thick beside the stalls nearest the stairs. The clear path ran past the body and ended at the advertising recess. Quinn went back to the victim. She held out her hand to the photographer, and he passed her a clear evidence ruler. Without touching the body, she set it beside the narrow drag mark. Twelve centimetres. The distance between the wheel tracks matched the distance between the legs of a folded display rack nearby, but the rack stood upright and left no trail through the dust. “Any carts down here?” she asked. “Two. Both by the stairs.” “And both too wide.” Patel scanned the stalls. “I can have the team measure the lot.” Quinn looked at the man’s open left hand. The green stain followed the shape of his fingers, as if he had held the compass there first. His right fist had closed over it later. She moved around him, careful to keep clear of the chalk, until the advertising recess lined up with the compass needle. Patel joined her. “You think someone used that thing to find the door?” “I think he held on to it while he died.” The needle remained fixed on the faded family in the poster. Quinn studied their faces, then the blue sky printed behind them. One corner had lifted from the backing. A strip of blue paper showed beneath it, the same shade as the fragment under the victim’s nails. She raised her hand towards the loose corner and stopped. “Photograph this first. Then ask for a ladder.” Patel looked from the poster to the dead man. “For a scrap of paper?” “For whatever he reached.”
created_at"2026-09-23T16:30:06.806Z" 9/23/2026, 4:30:06 PM
model"openai/gpt-6-sol-20260922"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency12211
moderation_latency(null)
generation_time51749
tokens_prompt1112
tokens_completion2487
native_tokens_prompt1009
native_tokens_completion2388
native_tokens_completion_images0
native_tokens_reasoning358
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"
usage0.025898
router(null)
user_agent"langchainjs-openai/1.0.0 ((node/v24.20.0; linux; x64))"
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request_id"req-1790181006-BKtNaEt3tOq3J1aNIRiV"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1790181006-NGuemlWmfG4RAWeQxPcu"
upstream_id"resp_0aba555a6cd74ab4016ab3fe8eee6487d1842e68a5322e4b27"
provider_responses
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endpoint_id"1cfc7d9d-4404-4b8e-9ee4-58ae45c9dcd4"
id"resp_0aba555a6cd74ab4016ab3fe8eee6487d1842e68a5322e4b27"
is_byokfalse
latency1104
model_permaslug"openai/gpt-6-sol-20260922"
provider_name"OpenAI"
status200
total_cost0.025898
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
totalTags10
adverbTagCount0
adverbTags(empty)
dialogueSentences64
tagDensity0.156
leniency0.313
rawRatio0
effectiveRatio0
100.00% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount1615
totalAiIsmAdverbs0
found(empty)
highlights(empty)
80.00% AI-ism character names
Target: 0 AI-default names (17 tracked, −20% each)
codexExemptions(empty)
found
0"Patel"
100.00% AI-ism location names
Target: 0 AI-default location names (33 tracked, −20% each)
codexExemptions(empty)
found(empty)
87.62% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount1615
totalAiIsms4
found
0
word"electric"
count2
1
word"velvet"
count1
2
word"scanned"
count1
highlights
0"electric"
1"velvet"
2"scanned"
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
emotionTells0
narrationSentences126
matches(empty)
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount1
hedgeCount0
narrationSentences126
filterMatches
0"watch"
hedgeMatches(empty)
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences180
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen41
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords1615
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions7
unquotedAttributions0
matches(empty)
59.48% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions49
wordCount1160
uniqueNames7
maxNameDensity1.81
worstName"Quinn"
maxWindowNameDensity3
worstWindowName"Patel"
discoveredNames
Harlow1
Quinn21
Camden1
Sergeant1
Nikhil1
Patel21
Dust3
persons
0"Harlow"
1"Quinn"
2"Sergeant"
3"Nikhil"
4"Patel"
5"Dust"
places(empty)
globalScore0.595
windowScore0.667
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences94
glossingSentenceCount0
matches(empty)
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount1615
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount1
totalSentences180
matches
0"saw that someone"
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs84
mean19.23
std17.12
cv0.89
sampleLengths
039
110
29
323
43
54
668
722
85
944
104
1145
1237
133
1415
1548
167
172
1836
197
2013
2112
2217
2317
2440
254
268
2744
2810
2911
3046
3117
326
337
3439
3529
3612
371
3849
399
405
4115
427
432
449
4559
468
473
4834
499
100.00% Passive voice overuse
Target: ≤2% passive sentences
passiveCount0
totalSentences126
matches(empty)
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount0
totalVerbs181
matches(empty)
100.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount0
semicolonCount1
flaggedSentences1
totalSentences180
ratio0.006
matches
0"Three officers had crossed the platform to get there; their blue overshoes had left faint prints in the dust."
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount1163
adjectiveStacks0
stackExamples(empty)
adverbCount14
adverbRatio0.012037833190025795
lyAdverbCount4
lyAdverbRatio0.0034393809114359416
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences180
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences180
mean8.97
std5.83
cv0.65
sampleLengths
019
110
210
310
49
511
612
73
84
97
106
1111
126
139
1429
1510
1612
175
1811
1914
2012
217
224
2332
2413
255
267
274
288
2913
303
314
3211
337
3414
3513
366
378
387
392
405
417
4211
4313
443
454
4613
4712
487
497
65.37% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats4
diversityRatio0.40555555555555556
totalSentences180
uniqueOpeners73
0.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount0
totalSentences114
matches(empty)
ratio0
100.00% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount21
totalSentences114
matches
0"Her colleague crouched beside the"
1"He wore a dark coat"
2"She bent towards the mug"
3"She could pick out the"
4"His right hand gripped a"
5"She checked her worn leather"
6"She turned her head to"
7"It ran across two tiles,"
8"He pressed a gloved fingertip"
9"He moved his torch across"
10"It ended at the lip"
11"She pointed to the ladder"
12"She followed the drag mark"
13"Its edges stayed straight for"
14"It led towards the advertising"
15"She pictured a loaded trolley"
16"She pressed her knuckles against"
17"She held out her hand"
18"His right fist had closed"
19"She moved around him, careful"
ratio0.184
34.56% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount97
totalSentences114
matches
0"Detective Harlow Quinn reached the"
1"Someone had fitted the barrier"
2"A constable stood beside it,"
3"A hole ran through its"
4"Quinn checked the latch, then"
5"The constable stayed at his"
6"Canvas awnings crowded the old"
7"Someone had cleared most of"
8"Her colleague crouched beside the"
9"Detective Sergeant Nikhil Patel looked"
10"Quinn stopped short of a"
11"The victim lay across it,"
12"He wore a dark coat"
13"Blood had dried beneath his"
14"Patel stood and pulled off"
15"Quinn looked along the platform."
16"Another offered WINTER APPLES, though"
17"A price card beside a"
18"Patel followed her gaze."
19"A forensic photographer moved around"
ratio0.851
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences114
matches(empty)
ratio0
100.00% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences51
technicalSentenceCount1
matches
0"Someone had cleared most of the goods but left behind a smell that caught in Quinn’s throat: lamp oil, wet stone, crushed herbs and the copper tang of blood."
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags10
uselessAdditionCount0
matches(empty)
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags5
fancyCount0
fancyTags(empty)
dialogueSentences64
tagDensity0.078
leniency0.156
rawRatio0
effectiveRatio0
87.5677%