Triple

T16979640
Position Surface form Disambiguated ID Type / Status
Subject María E411909 entity
Predicate derivedFrom P909 FINISHED
Object Mary
Mary is a common female given name of Hebrew origin, widely used in Christian and Western cultures and associated with numerous religious and historical 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: [María, derivedFrom, Mary]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mary
Context triple: [María, derivedFrom, Mary]
  • A. Mary
    Mary is a fictional character in B.F. Skinner’s utopian novel "Walden Two," representing one of the community’s young members shaped by its behaviorist social principles.
  • B. Mary
    Mary is the middle name of Edith Tolkien, the wife of author J.R.R. Tolkien.
  • C. 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.
  • D. Mary
    Mary is the birth name of American actress, comedian, and writer Lily Tomlin, known for her groundbreaking work in television, film, and theater.
  • E. Mary
    Mary is a film featuring Italian actor Marco Leonardi, known for his roles in internationally acclaimed cinema.
  • 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: [María, derivedFrom, Mary]
Generated description
Mary is a common female given name of Hebrew origin, widely used in Christian and Western cultures and associated with numerous religious and historical figures.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Mary
Target entity description: Mary is a common female given name of Hebrew origin, widely used in Christian and Western cultures and associated with numerous religious and historical 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 a central figure in Christianity, venerated as the mother of Jesus and often honored as the Virgin Mary.
  • C. Mary
    Mary is the given name of Mary Sidney, an English Renaissance noblewoman, writer, and literary patron.
  • D. Mary
    Mary is the given name of Mary Wollstonecraft, the pioneering 18th-century English writer and advocate of women's rights.
  • E. 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."
  • 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_69d886ca8f348190812768ea8d5055ce completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e3d1866bf48190a0ea15c377bf782c completed April 18, 2026, 6:46 p.m.
NED1 Entity disambiguation (via context triple) batch_6a00d477f7ec81909f1f0243004c9050 completed May 10, 2026, 6:54 p.m.
NEDg Description generation batch_6a00d5503be88190ac15a327ff3782ec completed May 10, 2026, 6:58 p.m.
NED2 Entity disambiguation (via description) batch_6a00d66e450c8190ae0befd3d7875ed8 completed May 10, 2026, 7:03 p.m.
Created at: April 10, 2026, 5:32 a.m.