Triple

T4146549
Position Surface form Disambiguated ID Type / Status
Subject As the World Turns E89796 entity
Predicate notableCharacter P1481 FINISHED
Object Lisa Miller Hughes
Lisa Miller Hughes is a long-running, central character on the American soap opera "As the World Turns," known for her dramatic relationships and evolving role over decades on the show.
 E445608 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: Lisa Miller Hughes | Statement: [As the World Turns, notableCharacter, Lisa Miller Hughes]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Lisa Miller Hughes
Context triple: [As the World Turns, notableCharacter, Lisa Miller Hughes]
  • A. Laura Hughes
    Laura Hughes is known as the sister of American Olympic figure skater Sarah Hughes.
  • B. Mary Beth Hughes
    Mary Beth Hughes was an American film and television actress best known for her roles in 1940s Hollywood dramas and crime films.
  • C. Amy Pastarnack Hughes
    Amy Pastarnack Hughes is best known as the mother of American Olympic figure skating champion Sarah Hughes.
  • D. Lorraine Miller
    Lorraine Miller was an American actress and dancer active in Hollywood films during the 1940s and 1950s.
  • E. Wendy Hughes
    Wendy Hughes was an acclaimed Australian actress known for her versatile performances in film, television, and theatre from the 1970s onward.
  • 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: Lisa Miller Hughes
Triple: [As the World Turns, notableCharacter, Lisa Miller Hughes]
Generated description
Lisa Miller Hughes is a long-running, central character on the American soap opera "As the World Turns," known for her dramatic relationships and evolving role over decades on the show.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Lisa Miller Hughes
Target entity description: Lisa Miller Hughes is a long-running, central character on the American soap opera "As the World Turns," known for her dramatic relationships and evolving role over decades on the show.
  • A. Laura Hughes
    Laura Hughes is known as the sister of American Olympic figure skater Sarah Hughes.
  • B. Mary Beth Hughes
    Mary Beth Hughes was an American film and television actress best known for her roles in 1940s Hollywood dramas and crime films.
  • C. Amy Pastarnack Hughes
    Amy Pastarnack Hughes is best known as the mother of American Olympic figure skating champion Sarah Hughes.
  • D. Lorraine Miller
    Lorraine Miller was an American actress and dancer active in Hollywood films during the 1940s and 1950s.
  • E. Wendy Hughes
    Wendy Hughes was an acclaimed Australian actress known for her versatile performances in film, television, and theatre from the 1970s onward.
  • 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_69aed95a59a881909b26e70b42c6811a completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69af025fef088190b42515d0a854a1ae completed March 9, 2026, 5:24 p.m.
NED1 Entity disambiguation (via context triple) batch_69bb60e9709881909673b8980694902b completed March 19, 2026, 2:35 a.m.
NEDg Description generation batch_69bb6a458ea88190b7b98fb4f26a936b completed March 19, 2026, 3:15 a.m.
NED2 Entity disambiguation (via description) batch_69bb6acb7b888190a31c264b131a6687 completed March 19, 2026, 3:17 a.m.
Created at: March 9, 2026, 3:43 p.m.