Triple

T806280
Position Surface form Disambiguated ID Type / Status
Subject Arnold Schwarzenegger E17440 entity
Predicate notableWork P4 FINISHED
Object Junior
Junior is a 1994 comedy film in which Arnold Schwarzenegger plays a scientist who becomes pregnant as part of an experimental fertility project.
 E95772 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: Junior | Statement: [Arnold Schwarzenegger, notableWork, Junior]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Junior
Context triple: [Arnold Schwarzenegger, notableWork, Junior]
  • A. Young
    Young is a regional town in New South Wales, Australia, historically known for its gold rush heritage and cherry production.
  • B. Min
    Min is a common given name of Chinese origin used for both males and females.
  • C. Entered Apprentice
    Entered Apprentice is the first and introductory degree of Freemasonry, representing a candidate’s initial initiation into the Masonic fraternity.
  • D. Child
    Child is a common English surname borne by various notable individuals, including the famed American chef and television personality Julia Child.
  • E. Junior Combination Room
    The Junior Combination Room at Peterhouse, Cambridge is the undergraduate student body and social organization representing and providing facilities for Peterhouse’s undergraduate community.
  • 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: Junior
Triple: [Arnold Schwarzenegger, notableWork, Junior]
Generated description
Junior is a 1994 comedy film in which Arnold Schwarzenegger plays a scientist who becomes pregnant as part of an experimental fertility project.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Junior
Target entity description: Junior is a 1994 comedy film in which Arnold Schwarzenegger plays a scientist who becomes pregnant as part of an experimental fertility project.
  • A. Young
    Young is a regional town in New South Wales, Australia, historically known for its gold rush heritage and cherry production.
  • B. Min
    Min is a common given name of Chinese origin used for both males and females.
  • C. Entered Apprentice
    Entered Apprentice is the first and introductory degree of Freemasonry, representing a candidate’s initial initiation into the Masonic fraternity.
  • D. Child
    Child is a common English surname borne by various notable individuals, including the famed American chef and television personality Julia Child.
  • E. Junior Combination Room
    The Junior Combination Room at Peterhouse, Cambridge is the undergraduate student body and social organization representing and providing facilities for Peterhouse’s undergraduate community.
  • 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_69a4937ae8a08190b5084a03d532b30e completed March 1, 2026, 7:28 p.m.
NER Named-entity recognition batch_69a4aac1142881908f6f78bfdb887930 completed March 1, 2026, 9:08 p.m.
NED1 Entity disambiguation (via context triple) batch_69a68927ba948190999fd1459dd62cca completed March 3, 2026, 7:09 a.m.
NEDg Description generation batch_69a6dcb16cf4819084f28de3328a0073 completed March 3, 2026, 1:05 p.m.
NED2 Entity disambiguation (via description) batch_69a75e9333e48190914827eb0267abd0 completed March 3, 2026, 10:20 p.m.
Created at: March 1, 2026, 7:38 p.m.