Triple

T18222230
Position Surface form Disambiguated ID Type / Status
Subject randomForest E436333 entity
Predicate author P4 FINISHED
Object Matthew Wiener
Matthew Wiener is a statistician and software developer known for his contributions to the R community, including work on the randomForest package for machine learning.
 E1313009 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: Matthew Wiener | Statement: [randomForest, author, Matthew Wiener]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Matthew Wiener
Context triple: [randomForest, author, Matthew Wiener]
  • A. Matthew Weiner
    Matthew Weiner is an American television writer, director, and producer best known for creating the critically acclaimed series "Mad Men."
  • B. Donald Faison
    Donald Faison is an American actor and comedian best known for his role as Dr. Christopher Turk on the television series "Scrubs."
  • C. John Slattery
    John Slattery is an American actor and director best known for his role as Roger Sterling on the television series "Mad Men."
  • D. Peter Krause
    Peter Krause is an American actor best known for his leading roles in television dramas such as Six Feet Under, Sports Night, and Parenthood.
  • E. Mark Kunis
    Mark Kunis is the father of actress Mila Kunis, known publicly mainly through his relationship to his famous daughter.
  • 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: Matthew Wiener
Triple: [randomForest, author, Matthew Wiener]
Generated description
Matthew Wiener is a statistician and software developer known for his contributions to the R community, including work on the randomForest package for machine learning.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Matthew Wiener
Target entity description: Matthew Wiener is a statistician and software developer known for his contributions to the R community, including work on the randomForest package for machine learning.
  • A. Matthew Weiner
    Matthew Weiner is an American television writer, director, and producer best known for creating the critically acclaimed series "Mad Men."
  • B. Donald Faison
    Donald Faison is an American actor and comedian best known for his role as Dr. Christopher Turk on the television series "Scrubs."
  • C. John Slattery
    John Slattery is an American actor and director best known for his role as Roger Sterling on the television series "Mad Men."
  • D. Peter Krause
    Peter Krause is an American actor best known for his leading roles in television dramas such as Six Feet Under, Sports Night, and Parenthood.
  • E. Mark Kunis
    Mark Kunis is the father of actress Mila Kunis, known publicly mainly through his relationship to his famous daughter.
  • 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_69d8b9103a8081908bbb0836fef10efd completed April 10, 2026, 8:47 a.m.
NER Named-entity recognition batch_69e4e47c85108190bd9707b40bdfdb38 completed April 19, 2026, 2:19 p.m.
NED1 Entity disambiguation (via context triple) batch_6a039f1da8948190811c2dc441b63866 completed May 12, 2026, 9:43 p.m.
NEDg Description generation batch_6a03a00195b08190b77a676bc44d1887 completed May 12, 2026, 9:47 p.m.
NED2 Entity disambiguation (via description) batch_6a03a0a4ef148190b6c69f72f019964a completed May 12, 2026, 9:50 p.m.
Created at: April 10, 2026, 10:32 a.m.