Triple

T17889761
Position Surface form Disambiguated ID Type / Status
Subject Dick E447283 entity
Predicate mainCharacter P1183 FINISHED
Object Arlene Lorenzo
Arlene Lorenzo is a fictional character who serves as the primary protagonist in the story featuring Dick.
E1302195 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: Arlene Lorenzo | Statement: [Dick, mainCharacter, Arlene Lorenzo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Arlene Lorenzo
Context triple: [Dick, mainCharacter, Arlene Lorenzo]
  • A. Arlene Oliva
    Arlene Oliva is best known as the wife of legendary Cuban bodybuilder and three-time Mr. Olympia champion Sergio Oliva.
  • B. Arlene DelValle
    Arlene DelValle is a film and television producer known for her work on the project "My Life."
  • C. Arlene Miles
    Arlene Miles was the first wife of American jazz singer and songwriter Mel Tormé.
  • D. Maria Conchita Alonso
    Maria Conchita Alonso is a Cuban-Venezuelan actress and singer known for her work in both Latin American and Hollywood films and television.
  • E. Brenda Patimkin
    Brenda Patimkin is a central character in Philip Roth's novella "Goodbye, Columbus," portrayed as a young, affluent Jewish woman whose relationship with the protagonist exposes tensions of class, identity, and assimilation in mid-20th-century American suburbia.
  • 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: Arlene Lorenzo
Triple: [Dick, mainCharacter, Arlene Lorenzo]
Generated description
Arlene Lorenzo is a fictional character who serves as the primary protagonist in the story featuring Dick.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Arlene Lorenzo
Target entity description: Arlene Lorenzo is a fictional character who serves as the primary protagonist in the story featuring Dick.
  • A. Arlene Oliva
    Arlene Oliva is best known as the wife of legendary Cuban bodybuilder and three-time Mr. Olympia champion Sergio Oliva.
  • B. Arlene DelValle
    Arlene DelValle is a film and television producer known for her work on the project "My Life."
  • C. Arlene Miles
    Arlene Miles was the first wife of American jazz singer and songwriter Mel Tormé.
  • D. Maria Conchita Alonso
    Maria Conchita Alonso is a Cuban-Venezuelan actress and singer known for her work in both Latin American and Hollywood films and television.
  • E. Brenda Patimkin
    Brenda Patimkin is a central character in Philip Roth's novella "Goodbye, Columbus," portrayed as a young, affluent Jewish woman whose relationship with the protagonist exposes tensions of class, identity, and assimilation in mid-20th-century American suburbia.
  • 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_69d8b9f59bd48190a6fc925a855b8bac completed April 10, 2026, 8:51 a.m.
NER Named-entity recognition batch_69e49d7828b481909b645fceb37a7ca3 completed April 19, 2026, 9:16 a.m.
NED1 Entity disambiguation (via context triple) batch_6a03499bb404819094ab71ee6745b896 completed May 12, 2026, 3:39 p.m.
NEDg Description generation batch_6a034ae2800881909d9a877d95e108c8 completed May 12, 2026, 3:44 p.m.
NED2 Entity disambiguation (via description) batch_6a034b543be08190abe8e8872c834c2b completed May 12, 2026, 3:46 p.m.
Created at: April 10, 2026, 10:18 a.m.