Triple

T944451
Position Surface form Disambiguated ID Type / Status
Subject The Testaments E20380 entity
Predicate featuresCharacter P626 FINISHED
Object Nicole
Nicole is a central character in Margaret Atwood's dystopian novel "The Testaments," whose story helps expose and challenge the oppressive regime of Gilead.
 E146703 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: Nicole | Statement: [The Testaments, featuresCharacter, Nicole]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Nicole
Context triple: [The Testaments, featuresCharacter, Nicole]
  • A. Nina
    Nina is a Danish fashion model best known for her appearances in the Sports Illustrated Swimsuit Issue and various high-profile advertising campaigns.
  • B. Vanessa
    Vanessa is an English feminine given name that gained wider recognition through public figures such as Vanessa Trump.
  • C. Paula
    Paula is a feminine given name used in many languages, derived from the Latin name Paulus meaning "small" or "humble."
  • D. Cynthia
    Cynthia is a common feminine given name used in various cultures, often associated with the Greek moon goddess Artemis.
  • E. Kimberly
    Kimberly is a feminine given name of English origin that has been widely used in the United States since the mid-20th century.
  • 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: Nicole
Triple: [The Testaments, featuresCharacter, Nicole]
Generated description
Nicole is a central character in Margaret Atwood's dystopian novel "The Testaments," whose story helps expose and challenge the oppressive regime of Gilead.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Nicole
Target entity description: Nicole is a central character in Margaret Atwood's dystopian novel "The Testaments," whose story helps expose and challenge the oppressive regime of Gilead.
  • A. Nina
    Nina is a Danish fashion model best known for her appearances in the Sports Illustrated Swimsuit Issue and various high-profile advertising campaigns.
  • B. Vanessa
    Vanessa is an English feminine given name that gained wider recognition through public figures such as Vanessa Trump.
  • C. Paula
    Paula is a feminine given name used in many languages, derived from the Latin name Paulus meaning "small" or "humble."
  • D. Cynthia
    Cynthia is a common feminine given name used in various cultures, often associated with the Greek moon goddess Artemis.
  • E. Kimberly
    Kimberly is a feminine given name of English origin that has been widely used in the United States since the mid-20th century.
  • 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_69a493b0270c81909e6c9ce310f6aa55 completed March 1, 2026, 7:29 p.m.
NER Named-entity recognition batch_69a4b3a3ed3881908386af140477c514 completed March 1, 2026, 9:46 p.m.
NED1 Entity disambiguation (via context triple) batch_69aca2d3cb588190882a480c18147384 completed March 7, 2026, 10:12 p.m.
NEDg Description generation batch_69aca556501c8190abd8ae1cd679919a completed March 7, 2026, 10:23 p.m.
NED2 Entity disambiguation (via description) batch_69aca7753da0819098e50ae51e967eb5 completed March 7, 2026, 10:32 p.m.
Created at: March 1, 2026, 7:40 p.m.