Triple

T9305370
Position Surface form Disambiguated ID Type / Status
Subject Tom Skerritt E223870 entity
Predicate spouse P13 FINISHED
Object Sue Oran
Sue Oran is known as the former spouse of American actor Tom Skerritt.
 E790389 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: Sue Oran | Statement: [Tom Skerritt, spouse, Sue Oran]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Sue Oran
Context triple: [Tom Skerritt, spouse, Sue Oran]
  • A. Sue Snyder
    Sue Snyder is an American public figure and advocate best known as the wife of former Michigan Governor Rick Snyder and for her involvement in charitable and civic initiatives in the state.
  • B. Sue Gunter
    Sue Gunter was a Hall of Fame American women’s basketball coach best known for her long, successful tenure leading major collegiate programs and elevating the profile of the women’s game.
  • C. Sue Ann Kahn
    Sue Ann Kahn is an American flutist and music educator known for her performances of contemporary and chamber music.
  • D. Sue Wilkins
    Sue Wilkins is a central character in Arthur C. Clarke’s science fiction novel "A Fall of Moondust," known for her role in the lunar tourism disaster that drives the story’s plot.
  • E. Sue Brown
    Sue Brown is a spirited and charming young woman in P. G. Wodehouse’s Blandings Castle stories, notably involved in romantic and comedic entanglements.
  • 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: Sue Oran
Triple: [Tom Skerritt, spouse, Sue Oran]
Generated description
Sue Oran is known as the former spouse of American actor Tom Skerritt.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Sue Oran
Target entity description: Sue Oran is known as the former spouse of American actor Tom Skerritt.
  • A. Sue Snyder
    Sue Snyder is an American public figure and advocate best known as the wife of former Michigan Governor Rick Snyder and for her involvement in charitable and civic initiatives in the state.
  • B. Sue Gunter
    Sue Gunter was a Hall of Fame American women’s basketball coach best known for her long, successful tenure leading major collegiate programs and elevating the profile of the women’s game.
  • C. Sue Ann Kahn
    Sue Ann Kahn is an American flutist and music educator known for her performances of contemporary and chamber music.
  • D. Sue Wilkins
    Sue Wilkins is a central character in Arthur C. Clarke’s science fiction novel "A Fall of Moondust," known for her role in the lunar tourism disaster that drives the story’s plot.
  • E. Sue Brown
    Sue Brown is a spirited and charming young woman in P. G. Wodehouse’s Blandings Castle stories, notably involved in romantic and comedic entanglements.
  • 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_69ca8424d0f08190831e2e93c6533aeb completed March 30, 2026, 2:09 p.m.
NER Named-entity recognition batch_69cd1da7c1e08190af19169f5d806cde completed April 1, 2026, 1:29 p.m.
NED1 Entity disambiguation (via context triple) batch_69d0b2735d788190bd8f963562fb85a8 completed April 4, 2026, 6:40 a.m.
NEDg Description generation batch_69d0b4231b708190b8a4a342bc63e84d completed April 4, 2026, 6:48 a.m.
NED2 Entity disambiguation (via description) batch_69d0b4b8131c8190b2aa8be56925b7bc completed April 4, 2026, 6:50 a.m.
Created at: March 30, 2026, 7:36 p.m.