Triple

T4559833
Position Surface form Disambiguated ID Type / Status
Subject The Good Girls Revolt E120565 entity
Predicate creator P184 FINISHED
Object Dana Calvo
Dana Calvo is an American television writer and producer best known for creating the period drama series "Good Girls Revolt."
 E452267 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: Dana Calvo | Statement: [The Good Girls Revolt, creator, Dana Calvo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Dana Calvo
Context triple: [The Good Girls Revolt, creator, Dana Calvo]
  • A. Alexis Chávez
    Alexis Chávez is an Argentine Paralympic middle-distance runner known for competing in international para-athletics events.
  • B. Nadine Velazquez
    Nadine Velazquez is an American actress and model best known for her roles in the sitcom "My Name Is Earl" and the film "Flight."
  • C. Rachel Salas
    Rachel Salas is a central character in the science-fiction film "In Time," portrayed as the wealthy and protective mother of Sylvia Weis.
  • D. Katherine Martorell
    Katherine Martorell is a Chilean lawyer and politician known for her roles in public security and government, associated with the right-wing political sector.
  • E. Nancy Parras
    Nancy Parras is a central character in the television drama series "The District," portrayed as a dedicated and resourceful member of the Washington, D.C. police force.
  • 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: Dana Calvo
Triple: [The Good Girls Revolt, creator, Dana Calvo]
Generated description
Dana Calvo is an American television writer and producer best known for creating the period drama series "Good Girls Revolt."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Dana Calvo
Target entity description: Dana Calvo is an American television writer and producer best known for creating the period drama series "Good Girls Revolt."
  • A. Alexis Chávez
    Alexis Chávez is an Argentine Paralympic middle-distance runner known for competing in international para-athletics events.
  • B. Nadine Velazquez
    Nadine Velazquez is an American actress and model best known for her roles in the sitcom "My Name Is Earl" and the film "Flight."
  • C. Rachel Salas
    Rachel Salas is a central character in the science-fiction film "In Time," portrayed as the wealthy and protective mother of Sylvia Weis.
  • D. Katherine Martorell
    Katherine Martorell is a Chilean lawyer and politician known for her roles in public security and government, associated with the right-wing political sector.
  • E. Nancy Parras
    Nancy Parras is a central character in the television drama series "The District," portrayed as a dedicated and resourceful member of the Washington, D.C. police force.
  • 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_69bd4636f1648190a701445c2fcd9c17 completed March 20, 2026, 1:05 p.m.
NER Named-entity recognition batch_69bd582b871c8190be0b70c76d639000 completed March 20, 2026, 2:22 p.m.
NED1 Entity disambiguation (via context triple) batch_69bdc593eaf881908a9043366230b391 completed March 20, 2026, 10:09 p.m.
NEDg Description generation batch_69bdc5f0b52c8190bbfa2a6a22d56725 completed March 20, 2026, 10:10 p.m.
NED2 Entity disambiguation (via description) batch_69bdc63b1e0881908f861f7c9c5ce3ac completed March 20, 2026, 10:12 p.m.
Created at: March 20, 2026, 1:09 p.m.