Triple

T12912723
Position Surface form Disambiguated ID Type / Status
Subject Tunnel community E308899 entity
Predicate includesCharacter P5716 FINISHED
Object Mary
Mary is a character in the "Tunnel community" setting, known as one of the individuals living within its underground society.
 E1011033 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: [Tunnel community, includesCharacter, Mary]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Mary
Context triple: [Tunnel community, includesCharacter, Mary]
  • A. Mary
    Mary is the given name of the American suspense novelist Mary Higgins Clark, known for her bestselling mystery and thriller books.
  • 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 of Lancaster was a 14th-century English noblewoman, daughter of Henry, 3rd Earl of Lancaster, and a member of the influential House of Lancaster.
  • D. Mary
    Mary is the middle name of Joseph Plunkett, the Irish nationalist, poet, and 1916 Easter Rising leader.
  • E. Mary
    Mary is the birth name of American actress, comedian, and writer Lily Tomlin, known for her groundbreaking work in television, film, and theater.
  • 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: [Tunnel community, includesCharacter, Mary]
Generated description
Mary is a character in the "Tunnel community" setting, known as one of the individuals living within its underground society.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Mary
Target entity description: Mary is a character in the "Tunnel community" setting, known as one of the individuals living within its underground society.
  • 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 a minor character in Mark Twain's novel "The Adventures of Tom Sawyer," known as Tom's kind and well-behaved cousin.
  • C. Mary
    Mary is a central character in the romantic comedy film "About Time," known for her warm, quirky personality and her relationship with the time-traveling protagonist.
  • D. Mary
    Mary is a central female character in Bruce Springsteen's song "Thunder Road," symbolizing hope, escape, and the possibility of a new life.
  • E. Mary
    Mary is a fictional character portrayed by British actress Gemma Jones, known for her nuanced performances in film and television.
  • F. None of above. chosen

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_69d7bdf92b588190acdf2a2291ac4590 completed April 9, 2026, 2:55 p.m.
NER Named-entity recognition batch_69d9719f96248190b746f9d4a468560c completed April 10, 2026, 9:54 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6af57a8b88190a3f15a3e9e02d492 completed May 3, 2026, 2:13 a.m.
NEDg Description generation batch_69f6b0cb53848190a7aa38f38d20e44d completed May 3, 2026, 2:19 a.m.
NED2 Entity disambiguation (via description) batch_69f6b1aa191081908266128776a2147a completed May 3, 2026, 2:23 a.m.
Created at: April 9, 2026, 5:41 p.m.