Triple

T3295031
Position Surface form Disambiguated ID Type / Status
Subject Jeremy Piven E69193 entity
Predicate actedIn P1668 FINISHED
Object Singles
Singles is a 1992 romantic comedy-drama film set in Seattle that follows the intertwined love lives of young adults amid the early 1990s grunge music scene.
 E343477 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: Singles | Statement: [Jeremy Piven, actedIn, Singles]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Singles
Context triple: [Jeremy Piven, actedIn, Singles]
  • A. Singel
    Singel is a historic canal in Amsterdam that once served as the city’s medieval moat and now forms part of its iconic canal belt.
  • B. A-Sides
    A-Sides is Soundgarden’s 1997 greatest hits compilation album, spanning their most notable work from the late 1980s through the mid-1990s.
  • C. Hits
    Hits is a compilation album by Joni Mitchell that collects some of her most popular and accessible songs from across her career.
  • D. single "Lonely"
    "Lonely" is a melancholic pop single by producer Benny Blanco and singer Justin Bieber that reflects on the emotional cost of fame and isolation.
  • E. 31 Songs
    31 Songs is a non-fiction book by Nick Hornby in which he reflects on his life and emotions through essays about 31 of his favorite songs.
  • 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: Singles
Triple: [Jeremy Piven, actedIn, Singles]
Generated description
Singles is a 1992 romantic comedy-drama film set in Seattle that follows the intertwined love lives of young adults amid the early 1990s grunge music scene.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Singles
Target entity description: Singles is a 1992 romantic comedy-drama film set in Seattle that follows the intertwined love lives of young adults amid the early 1990s grunge music scene.
  • A. Singel
    Singel is a historic canal in Amsterdam that once served as the city’s medieval moat and now forms part of its iconic canal belt.
  • B. A-Sides
    A-Sides is Soundgarden’s 1997 greatest hits compilation album, spanning their most notable work from the late 1980s through the mid-1990s.
  • C. Hits
    Hits is a compilation album by Joni Mitchell that collects some of her most popular and accessible songs from across her career.
  • D. single "Lonely"
    "Lonely" is a melancholic pop single by producer Benny Blanco and singer Justin Bieber that reflects on the emotional cost of fame and isolation.
  • E. 31 Songs
    31 Songs is a non-fiction book by Nick Hornby in which he reflects on his life and emotions through essays about 31 of his favorite songs.
  • 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_69ad859e529c8190a404273f53cb487d completed March 8, 2026, 2:20 p.m.
NER Named-entity recognition batch_69adb07661748190bf57469e101c5283 completed March 8, 2026, 5:23 p.m.
NED1 Entity disambiguation (via context triple) batch_69b2e86b8e04819091f4a4ae6d6a87ad completed March 12, 2026, 4:23 p.m.
NEDg Description generation batch_69b2e8f6a7c48190bc457f348c3a7179 completed March 12, 2026, 4:25 p.m.
NED2 Entity disambiguation (via description) batch_69b2e98804b8819097ef1fd498f13c9b completed March 12, 2026, 4:27 p.m.
Created at: March 8, 2026, 3:10 p.m.