Triple

T264744
Position Surface form Disambiguated ID Type / Status
Subject LinkedIn E5698 entity
Predicate keyPerson P256 FINISHED
Object Ryan Roslansky
Ryan Roslansky is the CEO of LinkedIn, known for leading the professional networking platform’s product and business strategy.
 E79554 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: Ryan Roslansky | Statement: [LinkedIn, keyPerson, Ryan Roslansky]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Ryan Roslansky
Context triple: [LinkedIn, keyPerson, Ryan Roslansky]
  • A. Matt Weitzman
    Matt Weitzman is an American television writer and producer best known as a co-creator and executive producer of the animated series "American Dad!"
  • B. Nathan Grossman
    Nathan Grossman is a Swedish documentary filmmaker best known for directing the climate activist portrait film "I Am Greta."
  • C. Sam Zussman
    Sam Zussman is a sports and media executive who serves as a top business leader for the NBA’s Brooklyn Nets organization.
  • D. Michael Filerman
    Michael Filerman was an American television producer best known for developing and producing popular prime-time soap operas during the 1970s and 1980s.
  • E. Chad Mirkin
    Chad Mirkin is an American chemist and nanotechnology pioneer known for inventing dip-pen nanolithography and developing spherical nucleic acids for biomedical applications.
  • 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: Ryan Roslansky
Triple: [LinkedIn, keyPerson, Ryan Roslansky]
Generated description
Ryan Roslansky is the CEO of LinkedIn, known for leading the professional networking platform’s product and business strategy.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Ryan Roslansky
Target entity description: Ryan Roslansky is the CEO of LinkedIn, known for leading the professional networking platform’s product and business strategy.
  • A. Matt Weitzman
    Matt Weitzman is an American television writer and producer best known as a co-creator and executive producer of the animated series "American Dad!"
  • B. Nathan Grossman
    Nathan Grossman is a Swedish documentary filmmaker best known for directing the climate activist portrait film "I Am Greta."
  • C. Sam Zussman
    Sam Zussman is a sports and media executive who serves as a top business leader for the NBA’s Brooklyn Nets organization.
  • D. Michael Filerman
    Michael Filerman was an American television producer best known for developing and producing popular prime-time soap operas during the 1970s and 1980s.
  • E. Chad Mirkin
    Chad Mirkin is an American chemist and nanotechnology pioneer known for inventing dip-pen nanolithography and developing spherical nucleic acids for biomedical applications.
  • 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_69a2587daeb081909591b9d30f80a271 completed Feb. 28, 2026, 2:52 a.m.
NER Named-entity recognition batch_69a25d8f9bbc8190a13841e4de093a66 completed Feb. 28, 2026, 3:14 a.m.
NED1 Entity disambiguation (via context triple) batch_69a56ee8472081908f3d3bed26a40aca completed March 2, 2026, 11:05 a.m.
NEDg Description generation batch_69a5714659dc8190aac2b41e4e149997 completed March 2, 2026, 11:15 a.m.
NED2 Entity disambiguation (via description) batch_69a571a98c208190872831a707419dc3 completed March 2, 2026, 11:16 a.m.
Created at: Feb. 28, 2026, 2:56 a.m.