Triple

T9647877
Position Surface form Disambiguated ID Type / Status
Subject Alison Owen E233252 entity
Predicate notableWork P4 FINISHED
Object Elizabeth
Elizabeth is a 1998 historical drama film about the early reign of Queen Elizabeth I of England, acclaimed for its performances and direction.
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: [Alison Owen, notableWork, Elizabeth]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Elizabeth
Context triple: [Alison Owen, notableWork, Elizabeth]
  • A. Elizabeth
    Elizabeth is the birth name of American actress and singer Betty Hutton, a popular Hollywood star of the 1940s and 1950s.
  • B. Elizabeth
    Elizabeth is the first name of Elizabeth Bishop, the acclaimed American poet known for her precise language and vivid imagery.
  • C. Elizabeth
    Elizabeth was the given name of Elizabeth Batts Cook, the wife of British explorer Captain James Cook.
  • D. Elizabeth
    Elizabeth is a comedic, high-strung fiancée character in the 1974 Mel Brooks film "Young Frankenstein," known for her dramatic personality and memorable scenes.
  • E. Elizabeth
    Elizabeth is the birth name of American actress and comedian Ellie Kemper, known for her roles in "The Office" and "Unbreakable Kimmy Schmidt."
  • 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: [Alison Owen, notableWork, Elizabeth]
Generated description
Elizabeth is a 1998 historical drama film about the early reign of Queen Elizabeth I of England, acclaimed for its performances and direction.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Elizabeth
Target entity description: Elizabeth is a 1998 historical drama film about the early reign of Queen Elizabeth I of England, acclaimed for its performances and direction.
  • 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 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.
  • D. Elizabeth
    Elizabeth is a biblical figure in the New Testament, known as the mother of John the Baptist and a relative of Mary, the mother of Jesus.
  • E. Elizabeth
    Elizabeth was the Duchess of York who later became Queen Elizabeth The Queen Mother, a prominent member of the British royal family in the 20th century.
  • 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_69ca848b31648190b57aa55da20285be completed March 30, 2026, 2:11 p.m.
NER Named-entity recognition batch_69cd9b826ff08190a972bdef84405f08 completed April 1, 2026, 10:26 p.m.
NED1 Entity disambiguation (via context triple) batch_69d1825dc8e08190bfc3475cd2e694ba completed April 4, 2026, 9:27 p.m.
NEDg Description generation batch_69d1837ec5548190a06458227fec2237 completed April 4, 2026, 9:32 p.m.
NED2 Entity disambiguation (via description) batch_69d183ea286c8190979d1429729f8abd completed April 4, 2026, 9:34 p.m.
Created at: March 30, 2026, 8:13 p.m.