Triple

T1714729
Position Surface form Disambiguated ID Type / Status
Subject Night Shift E37264 entity
Predicate hasShortStory P6847 FINISHED
Object Trucks
"Trucks" is a horror short story by Stephen King in which driverless, malevolent trucks besiege a group of people trapped at a remote truck stop.
 E192801 NE FINISHED

How this triple was built (4 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Trucks | Statement: [Night Shift, hasShortStory, Trucks]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Trucks
Context triple: [Night Shift, hasShortStory, Trucks]
  • A. Ram Trucks
    Ram Trucks is an American brand of light to heavy-duty pickup trucks and commercial vehicles known for its powerful performance and rugged design.
  • B. Cars
    Cars is a 2006 Pixar animated film that follows a hotshot race car who discovers friendship and humility in a forgotten desert town.
  • C. CAR
    CAR is a research center dedicated to advancing the understanding, diagnosis, and treatment of autism spectrum disorders through scientific study and clinical collaboration.
  • D. CAR
    CAR is the standard NHL abbreviation for the Carolina Hurricanes professional ice hockey team.
  • E. GM Truck and Coach
    GM Truck and Coach was General Motors’ division responsible for designing and manufacturing commercial trucks and transit buses, widely used across North America in the mid-20th century.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Trucks
Triple: [Night Shift, hasShortStory, Trucks]
Generated description
"Trucks" is a horror short story by Stephen King in which driverless, malevolent trucks besiege a group of people trapped at a remote truck stop.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Trucks
Target entity description: "Trucks" is a horror short story by Stephen King in which driverless, malevolent trucks besiege a group of people trapped at a remote truck stop.
  • A. Ram Trucks
    Ram Trucks is an American brand of light to heavy-duty pickup trucks and commercial vehicles known for its powerful performance and rugged design.
  • B. Cars
    Cars is a 2006 Pixar animated film that follows a hotshot race car who discovers friendship and humility in a forgotten desert town.
  • C. CAR
    CAR is a research center dedicated to advancing the understanding, diagnosis, and treatment of autism spectrum disorders through scientific study and clinical collaboration.
  • D. CAR
    CAR is the standard NHL abbreviation for the Carolina Hurricanes professional ice hockey team.
  • E. GM Truck and Coach
    GM Truck and Coach was General Motors’ division responsible for designing and manufacturing commercial trucks and transit buses, widely used across North America in the mid-20th century.
  • F. None of above. chosen

Provenance (5 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69a8861912dc8190931af43b4b9158a7 completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69abaffc4e5c81908ce0b9cfe833445e completed March 7, 2026, 4:56 a.m.
NED1 Entity disambiguation (via context triple) batch_69ad8ae10a048190b7a39e4fb4fbe224 completed March 8, 2026, 2:42 p.m.
NEDg Description generation batch_69ad957adf1c8190b7c8656c1984f998 completed March 8, 2026, 3:27 p.m.
NED2 Entity disambiguation (via description) batch_69ad97af6b388190b2af293599108df3 completed March 8, 2026, 3:37 p.m.
Created at: March 4, 2026, 7:30 p.m.