Triple

T13517594
Position Surface form Disambiguated ID Type / Status
Subject Michael Hirst E322805 entity
Predicate notableWork P4 FINISHED
Object Elizabeth
"Elizabeth" is a historical drama film written by Michael Hirst that chronicles the early reign of Queen Elizabeth I of England.
E64313 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: [Michael Hirst, notableWork, Elizabeth]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Elizabeth
Context triple: [Michael Hirst, notableWork, Elizabeth]
  • A. Elizabeth
    Elizabeth "Betty" Ford was the influential First Lady of the United States from 1974 to 1977, renowned for her advocacy on women's rights, breast cancer awareness, and addiction treatment.
  • B. Elizabeth
    Elizabeth is the middle name of Diane Elizabeth Dern, an individual likely known in relation to the Dern family.
  • C. Elizabeth
    Elizabeth is an alternate given name associated with Mary Surratt, the American boardinghouse owner convicted and executed for her role in the conspiracy to assassinate President Abraham Lincoln.
  • D. Elizabeth
    Elizabeth is the middle name of Princess Beatrice of York, a member of the British royal family.
  • E. Elizabeth
    Elizabeth is the full given name of Betsy McCaughey, an American politician, writer, and former lieutenant governor of New York.
  • 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: [Michael Hirst, notableWork, Elizabeth]
Generated description
"Elizabeth" is a historical drama film written by Michael Hirst that chronicles the early reign of Queen Elizabeth I of England.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Elizabeth
Target entity description: "Elizabeth" is a historical drama film written by Michael Hirst that chronicles the early reign of Queen Elizabeth I of England.
  • A. Elizabeth chosen
    "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.
  • B. Elizabeth
    Elizabeth is the first name of acclaimed New Zealand filmmaker Jane Campion, known for directing films such as "The Piano."
  • C. Elizabeth
    Elizabeth is the intelligent, witty, and strong-minded heroine of Jane Austen’s novel "Pride and Prejudice."
  • D. Elizabeth
    "Elizabeth" is a biographical work by J. Randy Taraborrelli that chronicles the life and career of actress Elizabeth Taylor.
  • E. Elizabeth
    Elizabeth is a central character in the 1931 horror film "Frankenstein," serving as Henry Frankenstein’s fiancée and a key figure whose vulnerability heightens the story’s emotional and dramatic stakes.
  • F. None of above.

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_69d80766a21881909f21a1b7421d3b8a completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69dbafa27f048190bed33a98e28c8d09 completed April 12, 2026, 2:43 p.m.
NED1 Entity disambiguation (via context triple) batch_69f75d8ab8548190a0a1adbe927c95d0 completed May 3, 2026, 2:36 p.m.
NEDg Description generation batch_69f75e7d8970819092116ae7a769ac21 completed May 3, 2026, 2:41 p.m.
NED2 Entity disambiguation (via description) batch_69f75f35c6008190b88e14feddb93a1d completed May 3, 2026, 2:44 p.m.
Created at: April 9, 2026, 9:44 p.m.