Triple

T4037611
Position Surface form Disambiguated ID Type / Status
Subject Dan Lin E83863 entity
Predicate employer P7 FINISHED
Object Rideback
Rideback is a film and television production company founded by producer Dan Lin, known for backing major Hollywood franchises and high-profile studio projects.
 E409657 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: Rideback | Statement: [Dan Lin, employer, Rideback]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Rideback
Context triple: [Dan Lin, employer, Rideback]
  • A. Vacallo
    Vacallo is a small municipality in the canton of Ticino in southern Switzerland, located near the Italian border.
  • B. Rider
    Rider is a cross-platform integrated development environment by JetBrains, widely used for .NET and C# development.
  • C. Darkhorse
    Darkhorse is the storied nickname of the U.S. Marine Corps’ 3rd Battalion, 5th Marines, renowned for its combat service and battlefield sacrifices.
  • D. Ramolino
    Ramolino is an Italian surname historically associated with Corsican nobility and notably borne by Letizia Ramolino, the mother of Napoleon Bonaparte.
  • E. Mo the Mule
    Mo the Mule is the costumed mule mascot representing the University of Central Missouri at its athletic events and campus activities.
  • 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: Rideback
Triple: [Dan Lin, employer, Rideback]
Generated description
Rideback is a film and television production company founded by producer Dan Lin, known for backing major Hollywood franchises and high-profile studio projects.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Rideback
Target entity description: Rideback is a film and television production company founded by producer Dan Lin, known for backing major Hollywood franchises and high-profile studio projects.
  • A. Vacallo
    Vacallo is a small municipality in the canton of Ticino in southern Switzerland, located near the Italian border.
  • B. Rider
    Rider is a cross-platform integrated development environment by JetBrains, widely used for .NET and C# development.
  • C. Darkhorse
    Darkhorse is the storied nickname of the U.S. Marine Corps’ 3rd Battalion, 5th Marines, renowned for its combat service and battlefield sacrifices.
  • D. Ramolino
    Ramolino is an Italian surname historically associated with Corsican nobility and notably borne by Letizia Ramolino, the mother of Napoleon Bonaparte.
  • E. Mo the Mule
    Mo the Mule is the costumed mule mascot representing the University of Central Missouri at its athletic events and campus activities.
  • 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_69aed92f7cf0819098e0539bdcc3767f completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aefb3656f08190aa5286d951013646 completed March 9, 2026, 4:54 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5564436788190aff89ebfeeed6d9b completed March 14, 2026, 12:36 p.m.
NEDg Description generation batch_69b5572b27c48190989311cef00b5f44 completed March 14, 2026, 12:40 p.m.
NED2 Entity disambiguation (via description) batch_69b55b333ffc8190a5df8b8d7bffa77f completed March 14, 2026, 12:57 p.m.
Created at: March 9, 2026, 3:36 p.m.