Triple

T19355937
Position Surface form Disambiguated ID Type / Status
Subject Mary Williamson Averell E484144 entity
Predicate givenName P17 FINISHED
Object Mary
Mary is a feminine given name of Hebrew origin, traditionally meaning "bitter" or "beloved" and widely used across many cultures and languages.
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 Williamson Averell, givenName, Mary]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mary
Context triple: [Mary Williamson Averell, 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 birth name of American actress, comedian, and writer Lily Tomlin, known for her groundbreaking work in television, film, and theater.
  • C. Mary
    Mary is the given name of the American stage and film actress Josephine Hull, known for her roles in classic mid-20th-century theater and cinema.
  • D. Mary
    Mary is a character in the "Tunnel community" setting, known as one of the individuals living within its underground society.
  • E. Mary
    Mary is the given first name of American actress, author, and radio host Marilu Henner.
  • 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 Williamson Averell, givenName, Mary]
Generated description
Mary is a feminine given name of Hebrew origin, traditionally meaning "bitter" or "beloved" and widely used across many cultures and languages.
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, traditionally meaning "bitter" or "beloved" and widely used across many cultures and languages.
  • 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 the given name of Mary J. Blige, the acclaimed American singer, songwriter, and actress often called the "Queen of Hip-Hop Soul."
  • 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 Cassatt, the renowned American Impressionist painter known for her depictions of women and 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_69d8e8d305088190ad13571532aa454c completed April 10, 2026, 12:10 p.m.
NER Named-entity recognition batch_69e61908920c8190833051e30ad2420c completed April 20, 2026, 12:16 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0733bfb3dc81908fe01c43583d0a57 completed May 15, 2026, 2:54 p.m.
NEDg Description generation batch_6a073566dfd8819097e5cee714ccebda completed May 15, 2026, 3:01 p.m.
NED2 Entity disambiguation (via description) batch_6a0735b445388190a588f6eeaf49e1b7 completed May 15, 2026, 3:03 p.m.
Created at: April 10, 2026, 1:34 p.m.