Triple

T34368864
Position Surface form Disambiguated ID Type / Status
Subject Peter Thorndyke E882096 entity
Predicate usesVehicleBrand P19188 FINISHED
Object Thorndyke Special
The Thorndyke Special is a custom-built racing car featured in Disney’s 1968 film “The Love Bug,” driven by the antagonist Peter Thorndyke in competition against Herbie.
 E2092923 NE FINISHED

How this triple was built (3 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: Thorndyke Special | Statement: [Peter Thorndyke, usesVehicleBrand, Thorndyke Special]
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: Thorndyke Special
Triple: [Peter Thorndyke, usesVehicleBrand, Thorndyke Special]
Generated description
The Thorndyke Special is a custom-built racing car featured in Disney’s 1968 film “The Love Bug,” driven by the antagonist Peter Thorndyke in competition against Herbie.
PD Predicate disambiguation gpt-5-mini-2025-08-07 
Target predicate: usesVehicleBrand
Context triple: [Peter Thorndyke, usesVehicleBrand, Thorndyke Special]
  • A. usesVehicleVariant
    Indicates that one entity performs an action or function by employing a specific variant or version of a vehicle.
  • B. vehicleUsed
    Indicates that a particular vehicle is utilized or employed in performing an action, event, or activity.
  • C. usesTransportBrand chosen
    Indicates that one entity makes use of a transportation service, vehicle, or system associated with a specific brand.
  • D. usedByVehicleType
    Indicates that something (such as a resource, component, or facility) is utilized or operated by a specific type or category of vehicle.
  • E. hasVehicularUse
    Indicates that something is used for, intended for, or associated with operation by vehicles or vehicular traffic.
  • F. None of above.

Provenance (6 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_69f349be5c9c81908dc726ae1f4c68f2 completed April 30, 2026, 12:23 p.m.
NER Named-entity recognition batch_69f71c35327c8190884f1bfe12bd2cd7 completed May 3, 2026, 9:58 a.m.
NED1 Entity disambiguation (via context triple) batch_6a3704ae23b881908d28cc58c0b8dd64 completed June 20, 2026, 9:22 p.m.
NEDg Description generation batch_6a3705a276b08190ad366c805fc6d4da completed June 20, 2026, 9:26 p.m.
NED2 Entity disambiguation (via description) batch_6a3706295cc48190b7c3753311f24fa6 completed June 20, 2026, 9:29 p.m.
PD Predicate disambiguation batch_69f71822d0e88190ac9731c7ae5a4def completed May 3, 2026, 9:40 a.m.
Created at: May 1, 2026, 1:58 a.m.