Triple

T1587598
Position Surface form Disambiguated ID Type / Status
Subject Mary Quinn Sullivan E34100 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: [Mary Quinn Sullivan, givenName, Mary]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mary
Context triple: [Mary Quinn Sullivan, givenName, Mary]
  • A. Mary
    Mary is a central figure in Christianity, venerated as the mother of Jesus and often honored as the Virgin Mary.
  • B. Mary
    Mary is the given first name of Margaret Truman, the daughter of U.S. President Harry S. Truman and a noted author and singer.
  • C. Mary
    Mary is the given first name of the acclaimed American actress Meryl Streep.
  • D. Mary
    Mary is a minor character in Mark Twain's novel "The Adventures of Tom Sawyer," known as Tom's kind and well-behaved cousin.
  • E. Mary
    Mary Eleanor Darwin was a member of the Darwin family, known primarily as a descendant of the naturalist Charles Darwin.
  • 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: [Mary Quinn Sullivan, 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 Shelley, the English novelist best known as the author of "Frankenstein."
  • E. Mary
    Mary is the given name of Mary Everest Boole, a 19th-century mathematics educator known for her innovative ideas on teaching mathematics, especially to children.
  • 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_69a885fceb2c8190b47e0f7c0aefbff0 completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69a9090b3a20819098fdb5605ee739d7 completed March 5, 2026, 4:39 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae02f8483c8190a72c79f5b60eb927 completed March 8, 2026, 11:15 p.m.
NEDg Description generation batch_69ae03e575a88190a93181eb9d9bc3eb completed March 8, 2026, 11:19 p.m.
NED2 Entity disambiguation (via description) batch_69ae0473aecc8190b3da07fb8fd18e81 completed March 8, 2026, 11:21 p.m.
Created at: March 4, 2026, 7:27 p.m.