Triple

T14982413
Position Surface form Disambiguated ID Type / Status
Subject Nadine Velazquez E373610 entity
Predicate playedCharacter P1507 FINISHED
Object Katerina Márquez
Katerina Márquez is a fictional character portrayed by Nadine Velazquez, best known as the flight attendant in the film "Flight."
 E1247291 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: Katerina Márquez | Statement: [Nadine Velazquez, playedCharacter, Katerina Márquez]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Katerina Márquez
Context triple: [Nadine Velazquez, playedCharacter, Katerina Márquez]
  • A. María Romo
    María Romo is a Spanish actress known for her work in film and television.
  • B. Catalina Álvarez del Casal
    Catalina Álvarez del Casal was a Colombian woman best known as the mother of independence leader Antonio Nariño.
  • C. Lucía García
    Lucía García is a Spanish professional footballer known for playing as a forward for top clubs and the Spain women’s national team.
  • D. María José Vega
    María José Vega is a Spanish scholar and literary critic known for her work on Renaissance and Baroque literature and the history of literary theory.
  • E. Miriam Gómez
    Miriam Gómez is a Cuban actress and writer best known as the longtime partner and literary collaborator of novelist Guillermo Cabrera Infante.
  • 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: Katerina Márquez
Triple: [Nadine Velazquez, playedCharacter, Katerina Márquez]
Generated description
Katerina Márquez is a fictional character portrayed by Nadine Velazquez, best known as the flight attendant in the film "Flight."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Katerina Márquez
Target entity description: Katerina Márquez is a fictional character portrayed by Nadine Velazquez, best known as the flight attendant in the film "Flight."
  • A. María Romo
    María Romo is a Spanish actress known for her work in film and television.
  • B. Catalina Álvarez del Casal
    Catalina Álvarez del Casal was a Colombian woman best known as the mother of independence leader Antonio Nariño.
  • C. Lucía García
    Lucía García is a Spanish professional footballer known for playing as a forward for top clubs and the Spain women’s national team.
  • D. María José Vega
    María José Vega is a Spanish scholar and literary critic known for her work on Renaissance and Baroque literature and the history of literary theory.
  • E. Miriam Gómez
    Miriam Gómez is a Cuban actress and writer best known as the longtime partner and literary collaborator of novelist Guillermo Cabrera Infante.
  • 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_69d85ccbbcd48190acb56e7cf104d8ad completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69ded6fe42a081909308f788fdf024d5 completed April 15, 2026, 12:08 a.m.
NED1 Entity disambiguation (via context triple) batch_6a01232149f88190b385fca6a7588d7b completed May 11, 2026, 12:30 a.m.
NEDg Description generation batch_6a0123ddb9148190af5037d834e1fe54 completed May 11, 2026, 12:33 a.m.
NED2 Entity disambiguation (via description) batch_6a01248b53d88190a4ec4fa6cee89bb1 completed May 11, 2026, 12:36 a.m.
Created at: April 10, 2026, 2:52 a.m.