Triple

T663461
Position Surface form Disambiguated ID Type / Status
Subject Vanessa Trump E12807 entity
Predicate givenName P17 FINISHED
Object Vanessa
Vanessa is an English feminine given name that gained wider recognition through public figures such as Vanessa Trump.
 E116721 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: Vanessa | Statement: [Vanessa Trump, givenName, Vanessa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Vanessa
Context triple: [Vanessa Trump, givenName, Vanessa]
  • A. Vivian
    Vivian "Buster" Burey Marshall was a civil rights activist and the first wife of U.S. Supreme Court Justice Thurgood Marshall.
  • B. Nina
    Nina is a Danish fashion model best known for her appearances in the Sports Illustrated Swimsuit Issue and various high-profile advertising campaigns.
  • C. Paula
    Paula is a feminine given name used in many languages, derived from the Latin name Paulus meaning "small" or "humble."
  • D. Carine
    Carine is a feminine given name, often considered a variant of names like Catherine or Karine, used in various European languages.
  • E. Sonia
    Sonia is a central female character in the romantic comedy film "Think Like a Man," whose relationships and personal growth intersect with the movie’s ensemble cast and themes about modern dating.
  • 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: Vanessa
Triple: [Vanessa Trump, givenName, Vanessa]
Generated description
Vanessa is an English feminine given name that gained wider recognition through public figures such as Vanessa Trump.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Vanessa
Target entity description: Vanessa is an English feminine given name that gained wider recognition through public figures such as Vanessa Trump.
  • A. Vivian
    Vivian "Buster" Burey Marshall was a civil rights activist and the first wife of U.S. Supreme Court Justice Thurgood Marshall.
  • B. Nina
    Nina is a Danish fashion model best known for her appearances in the Sports Illustrated Swimsuit Issue and various high-profile advertising campaigns.
  • C. Paula
    Paula is a feminine given name used in many languages, derived from the Latin name Paulus meaning "small" or "humble."
  • D. Carine
    Carine is a feminine given name, often considered a variant of names like Catherine or Karine, used in various European languages.
  • E. Sonia
    Sonia is a central female character in the romantic comedy film "Think Like a Man," whose relationships and personal growth intersect with the movie’s ensemble cast and themes about modern dating.
  • 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_69a493355dec819098d4244b2fa34885 completed March 1, 2026, 7:27 p.m.
NER Named-entity recognition batch_69a49fd1f0ec819087003d30bbab2fa6 completed March 1, 2026, 8:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac2573c6a48190bfe9b7f2ec026462 completed March 7, 2026, 1:17 p.m.
NEDg Description generation batch_69ac25dc9ee88190b7d7e72c6f7c8c76 completed March 7, 2026, 1:19 p.m.
NED2 Entity disambiguation (via description) batch_69ac2636ef5c8190909ff4e65c6787c5 completed March 7, 2026, 1:20 p.m.
Created at: March 1, 2026, 7:36 p.m.