Triple

T23317730
Position Surface form Disambiguated ID Type / Status
Subject Elizabeth (book) E590755 entity
Predicate title P38 FINISHED
Object Elizabeth
Elizabeth is a historical novel by J. Randy Taraborrelli that explores the life and reign of Queen Elizabeth II.
E1585284 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: Elizabeth | Statement: [Elizabeth (book), title, Elizabeth]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Elizabeth
Context triple: [Elizabeth (book), title, Elizabeth]
  • A. Elizabeth
    Elizabeth is the middle name of Tipper Gore, the American social issues advocate and former Second Lady of the United States.
  • B. Elizabeth
    Elizabeth was the birth name of Princess Elizabeth of England, who later became Queen Elizabeth I, the influential Tudor monarch of England.
  • C. Elizabeth
    Elizabeth is the birth name of American actress Beanie Feldstein, known for her roles in films like "Booksmart" and "Lady Bird."
  • D. Elizabeth
    Elizabeth is a fictional character in John Steinbeck’s novel "To a God Unknown," playing a key role in the protagonist’s family and the story’s exploration of faith, land, and sacrifice.
  • E. Elizabeth
    Elizabeth is the given name of Elizabeth Bacon Custer, an American author and the widow of U.S. Army officer George Armstrong Custer.
  • 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: Elizabeth
Triple: [Elizabeth (book), title, Elizabeth]
Generated description
Elizabeth is a historical novel by J. Randy Taraborrelli that explores the life and reign of Queen Elizabeth II.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Elizabeth
Target entity description: Elizabeth is a historical novel by J. Randy Taraborrelli that explores the life and reign of Queen Elizabeth II.
  • A. Elizabeth
    "Elizabeth" is a biographical work by J. Randy Taraborrelli that chronicles the life and career of actress Elizabeth Taylor.
  • B. Elizabeth
    Elizabeth is the given name of the English novelist Elizabeth Jane Howard, known for works such as the Cazalet Chronicles.
  • C. Elizabeth
    "Elizabeth" is a 1998 historical drama film that chronicles the early reign of Queen Elizabeth I of England, starring Cate Blanchett in the title role.
  • D. Elizabeth
    Elizabeth is the pen name of Elizabeth von Arnim, a British-born novelist best known for her semi-autobiographical and satirical works such as "Elizabeth and Her German Garden."
  • E. Elizabeth
    Elizabeth was the birth name of Princess Elizabeth of England, who later became Queen Elizabeth I, the influential Tudor monarch of England.
  • 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_69e25d1d32188190948eb76909d1dcc3 completed April 17, 2026, 4:17 p.m.
NER Named-entity recognition batch_69f197828c408190ae071624e40de4cc completed April 29, 2026, 5:30 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0c677a2370819083224ef1fafd9d59 completed May 19, 2026, 1:36 p.m.
NEDg Description generation batch_6a0c72132a608190b44c96de39b7187a completed May 19, 2026, 2:22 p.m.
NED2 Entity disambiguation (via description) batch_6a0c76c54ba08190be8f46bacb22b6a3 completed May 19, 2026, 2:42 p.m.
Created at: April 17, 2026, 5:06 p.m.