Triple

T6213471
Position Surface form Disambiguated ID Type / Status
Subject Marc Blitzstein E138926 entity
Predicate notableWork P4 FINISHED
Object Regina
Regina is a 1949 opera by American composer Marc Blitzstein, adapted from Lillian Hellman’s play "The Little Foxes."
 E575719 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: Regina | Statement: [Marc Blitzstein, notableWork, Regina]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Regina
Context triple: [Marc Blitzstein, notableWork, Regina]
  • A. Regina
    Regina is a fictional character known for her role as a maid.
  • B. Regina, Saskatchewan, Canada
    Regina, Saskatchewan, Canada is the capital city of the province of Saskatchewan, known as a major cultural and economic center on the Canadian Prairies.
  • C. Regina metropolitan area
    The Regina metropolitan area is the urban region centered on Regina, the capital city of Saskatchewan, Canada, encompassing the city and its surrounding communities.
  • D. Red Deer
    Red Deer is a mid-sized Canadian city in central Alberta known as a regional hub for agriculture, industry, and commerce between Calgary and Edmonton.
  • E. The Royal City
    The Royal City is the nickname of Guelph, a planned city in Ontario, Canada, known for its historic architecture, strong sense of community, and consistently high quality-of-life rankings.
  • 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: Regina
Triple: [Marc Blitzstein, notableWork, Regina]
Generated description
Regina is a 1949 opera by American composer Marc Blitzstein, adapted from Lillian Hellman’s play "The Little Foxes."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Regina
Target entity description: Regina is a 1949 opera by American composer Marc Blitzstein, adapted from Lillian Hellman’s play "The Little Foxes."
  • A. Regina
    Regina is a fictional character known for her role as a maid.
  • B. Regina, Saskatchewan, Canada
    Regina, Saskatchewan, Canada is the capital city of the province of Saskatchewan, known as a major cultural and economic center on the Canadian Prairies.
  • C. Regina metropolitan area
    The Regina metropolitan area is the urban region centered on Regina, the capital city of Saskatchewan, Canada, encompassing the city and its surrounding communities.
  • D. Red Deer
    Red Deer is a mid-sized Canadian city in central Alberta known as a regional hub for agriculture, industry, and commerce between Calgary and Edmonton.
  • E. The Royal City
    The Royal City is the nickname of Guelph, a planned city in Ontario, Canada, known for its historic architecture, strong sense of community, and consistently high quality-of-life rankings.
  • 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_69c008ada364819096c9e92c74d639b5 completed March 22, 2026, 3:20 p.m.
NER Named-entity recognition batch_69c0628c52ec8190b9c62c7fdc0aa83b completed March 22, 2026, 9:43 p.m.
NED1 Entity disambiguation (via context triple) batch_69c16f5cf41c8190b4efb1dc0a4a0e5e completed March 23, 2026, 4:50 p.m.
NEDg Description generation batch_69c1d54bffa881909edcd1342b8d8ce5 completed March 24, 2026, 12:05 a.m.
NED2 Entity disambiguation (via description) batch_69c1d7aec88081908bde0557888b5524 completed March 24, 2026, 12:15 a.m.
Created at: March 22, 2026, 4:21 p.m.