Triple

T10126480
Position Surface form Disambiguated ID Type / Status
Subject Der fliegende Holländer E226227 entity
Predicate character P662 FINISHED
Object Mary
Mary is a supporting character in Richard Wagner’s opera "Der fliegende Holländer," typically portrayed as Senta’s nurse or confidante within the story’s coastal Norwegian setting.
E843799 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: [Der fliegende Holländer, character, Mary]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mary
Context triple: [Der fliegende Holländer, character, 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 name of the American suspense novelist Mary Higgins Clark, known for her bestselling mystery and thriller books.
  • D. Mary
    Mary is the middle name of Katherine Mary Dewar, a component of her full personal name.
  • E. Mary
    Mary Allerton was a Mayflower passenger and one of the early settlers of Plymouth Colony in 17th-century New England.
  • 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: [Der fliegende Holländer, character, Mary]
Generated description
Mary is a supporting character in Richard Wagner’s opera "Der fliegende Holländer," typically portrayed as Senta’s nurse or confidante within the story’s coastal Norwegian setting.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Mary
Target entity description: Mary is a supporting character in Richard Wagner’s opera "Der fliegende Holländer," typically portrayed as Senta’s nurse or confidante within the story’s coastal Norwegian setting.
  • A. Mary
    Mary is a central character in Ralph Vaughan Williams's opera "Hugh the Drover," serving as the romantic interest whose choices drive much of the plot.
  • B. Mary
    Mary is a central character in Robert Frost's narrative poem "The Death of the Hired Man," serving as a compassionate and mediating presence between her husband Warren and the returning farmhand Silas.
  • C. Mary
    Mary is a central character in W. H. Auden’s long poem "For the Time Being," which reimagines the Nativity story in a modern, philosophical context.
  • 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 is a central female character in Bruce Springsteen's song "Thunder Road," symbolizing hope, escape, and the possibility of a new life.
  • 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_69ca843057b48190a86730167f5d6b98 completed March 30, 2026, 2:09 p.m.
NER Named-entity recognition batch_69cdd2eef7388190b95ffd02814f2d1f completed April 2, 2026, 2:22 a.m.
NED1 Entity disambiguation (via context triple) batch_69d2e5c29f6c8190b347a6963ca46dac completed April 5, 2026, 10:44 p.m.
NEDg Description generation batch_69d2e6f0aa988190aa9a866afcc2a1a2 completed April 5, 2026, 10:49 p.m.
NED2 Entity disambiguation (via description) batch_69d2e78384f48190abb7bdd7fcadcd9a completed April 5, 2026, 10:51 p.m.
Created at: March 30, 2026, 9:05 p.m.