Triple

T14567262
Position Surface form Disambiguated ID Type / Status
Subject Funny Games U.S. E341815 entity
Predicate leadCharacter P1668 FINISHED
Object Paul
Paul is one of the two sadistic young men who psychologically and physically torment a family in the home-invasion horror film "Funny Games U.S."
E1106758 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: Paul | Statement: [Funny Games U.S., leadCharacter, Paul]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Paul
Context triple: [Funny Games U.S., leadCharacter, Paul]
  • A. Paul
    Paul is a laid-back, charming sperm donor whose unexpected involvement with his biological children disrupts a lesbian couple’s family dynamic in the film "The Kids Are All Right."
  • B. Paul
    Paul is a 2011 sci-fi comedy film about two British geeks who encounter a wisecracking alien during a road trip across the United States.
  • C. Paul
    Paul is a village and civil parish in Cornwall, England, known for its historic church and coastal setting near Penzance.
  • D. Paul
    Paul is a character in the crime drama film "Never Die Alone," which follows the violent, intertwined lives of drug dealers and those around them.
  • E. Paul
    Paul is a character from the "Wild" universe, known for his role within its adventurous, nature-centered narrative.
  • 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: Paul
Triple: [Funny Games U.S., leadCharacter, Paul]
Generated description
Paul is one of the two sadistic young men who psychologically and physically torment a family in the home-invasion horror film "Funny Games U.S."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Paul
Target entity description: Paul is one of the two sadistic young men who psychologically and physically torment a family in the home-invasion horror film "Funny Games U.S."
  • A. Paul
    Paul is a central character in the psychological horror film "It Comes at Night," portrayed as a protective family man struggling to safeguard his loved ones amid a mysterious, apocalyptic threat.
  • B. Paul
    Paul is a laid-back, charming sperm donor whose unexpected involvement with his biological children disrupts a lesbian couple’s family dynamic in the film "The Kids Are All Right."
  • C. Paul
    Paul is a 2011 sci-fi comedy film about two British geeks who encounter a wisecracking alien during a road trip across the United States.
  • D. Paul
    Paul is the charismatic young con artist at the center of the play and film "Six Degrees of Separation," whose deception exposes themes of class, connection, and identity among wealthy New Yorkers.
  • E. Paul
    Paul is a character in the crime drama film "Never Die Alone," which follows the violent, intertwined lives of drug dealers and those around them.
  • 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_69d822dcc6248190bed689984bceb0e2 completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69deb38d89fc819086709fd3607b835f completed April 14, 2026, 9:37 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd8ac669cc819083e05620b1e8c370 completed May 8, 2026, 7:03 a.m.
NEDg Description generation batch_69fd8c5b09448190ad084746a6dd23f5 completed May 8, 2026, 7:10 a.m.
NED2 Entity disambiguation (via description) batch_69fd8d609684819090a9c3f2304f4a6a completed May 8, 2026, 7:14 a.m.
Created at: April 10, 2026, 1:23 a.m.