Triple

T11426848
Position Surface form Disambiguated ID Type / Status
Subject Madeline Kahn E270772 entity
Predicate spouse P13 FINISHED
Object John Hansbury
John Hansbury is best known as the husband of acclaimed American actress and comedian Madeline Kahn.
 E929585 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: John Hansbury | Statement: [Madeline Kahn, spouse, John Hansbury]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: John Hansbury
Context triple: [Madeline Kahn, spouse, John Hansbury]
  • A. Caspar Jopling
    Caspar Jopling is a British art dealer and Sotheby’s executive known publicly for his marriage to singer-songwriter Ellie Goulding.
  • B. William Ashburner
    William Ashburner was a 19th-century American mining engineer and geologist known for his work in mineral surveying and resource assessment in the western United States.
  • C. Barnard Hughes
    Barnard Hughes was an American character actor known for his work in film, television, and theater, often playing kindly or eccentric older men.
  • D. George Buckley
    George Buckley is a British businessman best known for serving as the chairman and CEO of 3M.
  • E. Henry Van Brunt
    Henry Van Brunt was a prominent 19th-century American architect known for his influential role in shaping civic and institutional architecture across the United States.
  • 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: John Hansbury
Triple: [Madeline Kahn, spouse, John Hansbury]
Generated description
John Hansbury is best known as the husband of acclaimed American actress and comedian Madeline Kahn.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: John Hansbury
Target entity description: John Hansbury is best known as the husband of acclaimed American actress and comedian Madeline Kahn.
  • A. Caspar Jopling
    Caspar Jopling is a British art dealer and Sotheby’s executive known publicly for his marriage to singer-songwriter Ellie Goulding.
  • B. William Ashburner
    William Ashburner was a 19th-century American mining engineer and geologist known for his work in mineral surveying and resource assessment in the western United States.
  • C. Barnard Hughes
    Barnard Hughes was an American character actor known for his work in film, television, and theater, often playing kindly or eccentric older men.
  • D. George Buckley
    George Buckley is a British businessman best known for serving as the chairman and CEO of 3M.
  • E. Henry Van Brunt
    Henry Van Brunt was a prominent 19th-century American architect known for his influential role in shaping civic and institutional architecture across the United States.
  • 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_69d6aadeef688190874bcecd88b3dd9b completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d806c000b88190bfaa646b2dc424b7 completed April 9, 2026, 8:06 p.m.
NED1 Entity disambiguation (via context triple) batch_69e624600e0081909cc6f53c05a34efb completed April 20, 2026, 1:04 p.m.
NEDg Description generation batch_69e62cf224f881908badcdab6aea1aef completed April 20, 2026, 1:41 p.m.
NED2 Entity disambiguation (via description) batch_69e663ffedfc8190a2b51995c62d1e6b completed April 20, 2026, 5:36 p.m.
Created at: April 8, 2026, 9:35 p.m.