Triple

T15993884
Position Surface form Disambiguated ID Type / Status
Subject Isabella Mary Mayson E387908 entity
Predicate givenName P17 FINISHED
Object Mary
Mary is a feminine given name of Hebrew origin, widely used in English-speaking countries and historically associated with numerous religious and cultural figures.
E75782 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: Mary | Statement: [Isabella Mary Mayson, givenName, Mary]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mary
Context triple: [Isabella Mary Mayson, givenName, Mary]
  • A. Mary
    Mary is the middle name of Edith Tolkien, the wife of author J.R.R. Tolkien.
  • B. Mary
    Mary is the given name of Mary Catherine Bateson, an American cultural anthropologist and writer known for her work on learning and the human life cycle.
  • C. Mary
    Mary is the birth name of American actress, comedian, and writer Lily Tomlin, known for her groundbreaking work in television, film, and theater.
  • D. Mary
    Mary is a film featuring Italian actor Marco Leonardi, known for his roles in internationally acclaimed cinema.
  • E. Mary
    Mary is the first name of Tipper Gore, the American social issues advocate and former Second Lady of the United States.
  • 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: Mary
Triple: [Isabella Mary Mayson, givenName, Mary]
Generated description
Mary is a feminine given name of Hebrew origin, widely used in English-speaking countries and historically associated with numerous religious and cultural figures.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Mary
Target entity description: Mary is a feminine given name of Hebrew origin, widely used in English-speaking countries and historically associated with numerous religious and cultural figures.
  • A. Mary chosen
    Mary is a feminine given name of Hebrew origin, widely used in English-speaking and many other cultures and historically associated with numerous religious and historical figures.
  • B. Mary
    Mary is the given name of Mary Wollstonecraft, the pioneering 18th-century English writer and advocate of women's rights.
  • C. Mary
    Mary is a central figure in Christianity, venerated as the mother of Jesus and often honored as the Virgin Mary.
  • D. Mary
    Mary is the given name of Mary Sidney, an English Renaissance noblewoman, writer, and literary patron.
  • E. Mary
    Mary is the given name of Mary Tyler Peabody, an American educator and reformer known for her work in the 19th 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_69d86daa562c81908aacc179c0fe8fb5 completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e15785347081908831b4cbc9a2dd45 completed April 16, 2026, 9:41 p.m.
NED1 Entity disambiguation (via context triple) batch_69ffc3d5d72081908aa235c5ad9b5707 completed May 9, 2026, 11:31 p.m.
NEDg Description generation batch_69ffc5444c1c8190854de5575b9ec1c5 completed May 9, 2026, 11:37 p.m.
NED2 Entity disambiguation (via description) batch_69ffc5b95290819098b28c44c22b2799 completed May 9, 2026, 11:39 p.m.
Created at: April 10, 2026, 4:55 a.m.