Triple

T9998817
Position Surface form Disambiguated ID Type / Status
Subject Dennis Farina E197270 entity
Predicate spouse P13 FINISHED
Object Patricia Farina
Patricia Farina is best known as the wife of the late American actor and former Chicago police officer Dennis Farina.
 E840284 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: Patricia Farina | Statement: [Dennis Farina, spouse, Patricia Farina]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Patricia Farina
Context triple: [Dennis Farina, spouse, Patricia Farina]
  • A. Lyda Roberti
    Lyda Roberti was a Polish-born American stage and film actress of the early 1930s, known for her comedic roles, distinctive accent, and appearances in Hollywood musicals and comedies.
  • B. Patricia Roccuzzo
    Patricia Roccuzzo is the maternal grandmother of Thiago Messi, the eldest son of footballer Lionel Messi and Antonela Roccuzzo.
  • C. Patricia Roc
    Patricia Roc was a popular British film actress of the 1940s, best known for her roles in melodramas and wartime dramas produced by major UK studios.
  • D. Patricia Danova
    Patricia Danova is known as the spouse of Italian-American actor Cesare Danova.
  • E. Myrna Fahey
    Myrna Fahey was an American actress known for her film and television roles in the 1950s and 1960s, often appearing in comedies and dramas.
  • 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: Patricia Farina
Triple: [Dennis Farina, spouse, Patricia Farina]
Generated description
Patricia Farina is best known as the wife of the late American actor and former Chicago police officer Dennis Farina.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Patricia Farina
Target entity description: Patricia Farina is best known as the wife of the late American actor and former Chicago police officer Dennis Farina.
  • A. Lyda Roberti
    Lyda Roberti was a Polish-born American stage and film actress of the early 1930s, known for her comedic roles, distinctive accent, and appearances in Hollywood musicals and comedies.
  • B. Patricia Roccuzzo
    Patricia Roccuzzo is the maternal grandmother of Thiago Messi, the eldest son of footballer Lionel Messi and Antonela Roccuzzo.
  • C. Patricia Roc
    Patricia Roc was a popular British film actress of the 1940s, best known for her roles in melodramas and wartime dramas produced by major UK studios.
  • D. Patricia Danova
    Patricia Danova is known as the spouse of Italian-American actor Cesare Danova.
  • E. Myrna Fahey
    Myrna Fahey was an American actress known for her film and television roles in the 1950s and 1960s, often appearing in comedies and dramas.
  • 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_69ca82f3b61c81908ecc2c1c96dbc2e4 completed March 30, 2026, 2:04 p.m.
NER Named-entity recognition batch_69cdcc8c78448190a5332f4ff8a7b3dd completed April 2, 2026, 1:55 a.m.
NED1 Entity disambiguation (via context triple) batch_69d2b5e243548190b77328b5ce9e8028 completed April 5, 2026, 7:20 p.m.
NEDg Description generation batch_69d2b741cad481909f04e2f8da68753c completed April 5, 2026, 7:25 p.m.
NED2 Entity disambiguation (via description) batch_69d2b805afa08190a43745d764a75050 completed April 5, 2026, 7:29 p.m.
Created at: March 30, 2026, 8:51 p.m.