Triple

T13240569
Position Surface form Disambiguated ID Type / Status
Subject The Little Hours E315267 entity
Predicate stars P1956 FINISHED
Object Lauren Weedman
Lauren Weedman is an American actress, comedian, and writer known for her sharp, character-driven comedy and appearances in film, television, and solo stage shows.
E1151249 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: Lauren Weedman | Statement: [The Little Hours, stars, Lauren Weedman]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lauren Weedman
Context triple: [The Little Hours, stars, Lauren Weedman]
  • A. Lauren Dolgen
    Lauren Dolgen is a television producer best known for developing and producing MTV’s teen pregnancy reality franchise, including "16 and Pregnant" and its spin-offs.
  • B. Lauren Lyle
    Lauren Lyle is a Scottish actress best known for her role as Marsali MacKimmie Fraser in the television series "Outlander."
  • C. Lauren Greer
    Lauren Greer is a notable individual recognized for achievements significant enough to be associated with the surname Greer.
  • D. Lauren Barber
    Lauren Barber is best known as the wife of English musician and actor Gary Kemp.
  • E. Lauren Nourse
    Lauren Nourse is an Australian former netball player best known for her career with the Queensland Firebirds in the ANZ Championship.
  • 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: Lauren Weedman
Triple: [The Little Hours, stars, Lauren Weedman]
Generated description
Lauren Weedman is an American actress, comedian, and writer known for her sharp, character-driven comedy and appearances in film, television, and solo stage shows.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Lauren Weedman
Target entity description: Lauren Weedman is an American actress, comedian, and writer known for her sharp, character-driven comedy and appearances in film, television, and solo stage shows.
  • A. Lauren Dolgen
    Lauren Dolgen is a television producer best known for developing and producing MTV’s teen pregnancy reality franchise, including "16 and Pregnant" and its spin-offs.
  • B. Lauren Lyle
    Lauren Lyle is a Scottish actress best known for her role as Marsali MacKimmie Fraser in the television series "Outlander."
  • C. Lauren Greer
    Lauren Greer is a notable individual recognized for achievements significant enough to be associated with the surname Greer.
  • D. Lauren Barber
    Lauren Barber is best known as the wife of English musician and actor Gary Kemp.
  • E. Lauren Nourse
    Lauren Nourse is an Australian former netball player best known for her career with the Queensland Firebirds in the ANZ Championship.
  • 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_69d806b1072881909e46bd212259c5f0 completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69d98d5850ac8190849a51da39efe5be completed April 10, 2026, 11:52 p.m.
NED1 Entity disambiguation (via context triple) batch_69ff01ce8f988190b503af0cd5f2a97e completed May 9, 2026, 9:43 a.m.
NEDg Description generation batch_69ff02a62dcc819087eddd2f0b4c29cb completed May 9, 2026, 9:47 a.m.
NED2 Entity disambiguation (via description) batch_69ff036153588190ae46fcde257eb3cb completed May 9, 2026, 9:50 a.m.
Created at: April 9, 2026, 9:23 p.m.