Triple

T20870528
Position Surface form Disambiguated ID Type / Status
Subject Hal Kanter E513877 entity
Predicate spouse P13 FINISHED
Object Sue Kanter
Sue Kanter is known as the wife of American comedy writer, producer, and director Hal Kanter.
 E1454893 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 Kanter | Statement: [Hal Kanter, spouse, Sue Kanter]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Sue Kanter
Context triple: [Hal Kanter, spouse, Sue Kanter]
  • A. Nancy Kanter
    Nancy Kanter is a television executive and producer best known for her leadership roles at Disney Junior and her work developing and overseeing acclaimed children’s programming.
  • B. Susanne Loeb
    Susanne Loeb is an individual notable enough to be recognized as a prominent bearer of the surname Loeb.
  • C. Carolyn Strauss
    Carolyn Strauss is an American television executive and producer best known for her influential work at HBO, including helping develop and produce acclaimed series such as Game of Thrones.
  • D. Pamela Segall
    Pamela Segall is an American actress and voice actress best known for voicing Bobby Hill on the animated television series "King of the Hill."
  • E. Melissa Corken
    Melissa Corken is a music industry figure best known as the founder of the World Music Awards, an international awards show recognizing global recording artists.
  • 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 Kanter
Triple: [Hal Kanter, spouse, Sue Kanter]
Generated description
Sue Kanter is known as the wife of American comedy writer, producer, and director Hal Kanter.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Sue Kanter
Target entity description: Sue Kanter is known as the wife of American comedy writer, producer, and director Hal Kanter.
  • A. Nancy Kanter
    Nancy Kanter is a television executive and producer best known for her leadership roles at Disney Junior and her work developing and overseeing acclaimed children’s programming.
  • B. Susanne Loeb
    Susanne Loeb is an individual notable enough to be recognized as a prominent bearer of the surname Loeb.
  • C. Carolyn Strauss
    Carolyn Strauss is an American television executive and producer best known for her influential work at HBO, including helping develop and produce acclaimed series such as Game of Thrones.
  • D. Pamela Segall
    Pamela Segall is an American actress and voice actress best known for voicing Bobby Hill on the animated television series "King of the Hill."
  • E. Melissa Corken
    Melissa Corken is a music industry figure best known as the founder of the World Music Awards, an international awards show recognizing global recording artists.
  • 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_69e0b4f675cc8190b4e745225b62eb66 completed April 16, 2026, 10:07 a.m.
NER Named-entity recognition batch_69e6c4637ec48190830023d20fb8124c completed April 21, 2026, 12:27 a.m.
NED1 Entity disambiguation (via context triple) batch_6a09139a9ef081909585c53823e12ca4 completed May 17, 2026, 1:02 a.m.
NEDg Description generation batch_6a09144734b0819090901b8e6590222f completed May 17, 2026, 1:05 a.m.
NED2 Entity disambiguation (via description) batch_6a0914a0fb4c8190be5d504ff65dfe32 completed May 17, 2026, 1:06 a.m.
Created at: April 16, 2026, 12:45 p.m.