Triple

T19865128
Position Surface form Disambiguated ID Type / Status
Subject Elizabeth Wordsworth E477371 entity
Predicate givenName P17 FINISHED
Object Elizabeth
Elizabeth is a feminine given name of Hebrew origin, widely used in English-speaking countries and borne by numerous historical and literary figures.
E40040 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 Wordsworth, givenName, Elizabeth]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Elizabeth
Context triple: [Elizabeth Wordsworth, givenName, Elizabeth]
  • A. Elizabeth
    Elizabeth is the given first name of American actress Bess Armstrong, known for her work in film and television since the late 1970s.
  • B. Elizabeth
    Elizabeth is the middle name of Tipper Gore, the American social issues advocate and former Second Lady of the United States.
  • C. Elizabeth
    Elizabeth was the birth name of Princess Elizabeth of England, who later became Queen Elizabeth I, the influential Tudor monarch of England.
  • D. Elizabeth
    Elizabeth is the birth name of American actress Beanie Feldstein, known for her roles in films like "Booksmart" and "Lady Bird."
  • E. 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.
  • 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 Wordsworth, givenName, Elizabeth]
Generated description
Elizabeth is a feminine given name of Hebrew origin, widely used in English-speaking countries and borne by numerous historical and literary figures.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Elizabeth
Target entity description: Elizabeth is a feminine given name of Hebrew origin, widely used in English-speaking countries and borne by numerous historical and literary figures.
  • A. Elizabeth chosen
    Elizabeth is a feminine given name of Hebrew origin, traditionally interpreted to mean "God is my oath" and widely used in many English-speaking and European cultures.
  • B. Elizabeth
    Elizabeth is the given name of the renowned Victorian-era English poet Elizabeth Barrett Browning.
  • C. Elizabeth
    Elizabeth was the birth name of Princess Elizabeth of England, who later became Queen Elizabeth I, the influential Tudor monarch of England.
  • D. Elizabeth
    Elizabeth is the middle name of Mary Elizabeth Horsley, likely reflecting a traditional English given name.
  • E. Elizabeth
    Elizabeth is the given name of Lady Elizabeth Spencer-Churchill, a member of the prominent Spencer-Churchill aristocratic family in Britain.
  • 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_69d8e51e7d948190aedbcd6c30361c39 completed April 10, 2026, 11:55 a.m.
NER Named-entity recognition batch_69e6589eb24081908715b683de1edc68 completed April 20, 2026, 4:47 p.m.
NED1 Entity disambiguation (via context triple) batch_6a07ea37b6f48190bf921fd101086d7e completed May 16, 2026, 3:53 a.m.
NEDg Description generation batch_6a07eb4daeb48190b31bf52ea1fa758c completed May 16, 2026, 3:58 a.m.
NED2 Entity disambiguation (via description) batch_6a07ebde7f9c8190b39f53d5598d8bd7 completed May 16, 2026, 4 a.m.
Created at: April 10, 2026, 1:51 p.m.