Triple

T3172503
Position Surface form Disambiguated ID Type / Status
Subject Terrence J E66385 entity
Predicate coHostWith P5275 FINISHED
Object Rocsi Diaz
Rocsi Diaz is an American television and radio personality best known as a former co-host of BET’s music video countdown show 106 & Park.
E345136 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: Rocsi Diaz | Statement: [Terrence J, coHostWith, Rocsi Diaz]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Rocsi Diaz
Context triple: [Terrence J, coHostWith, Rocsi Diaz]
  • A. Miluska Rosales
    Miluska Rosales is known as the spouse of Italian actor and television personality Rossano Rubicondi.
  • B. Silvia Navarro
    Silvia Navarro is a Mexican actress best known for her leading roles in popular telenovelas and television dramas.
  • C. Amada Cruz
    Amada Cruz is an American museum director and arts administrator known for leading major art institutions, including serving as director of the Seattle Art Museum.
  • D. Tatiana Gutierrez
    Tatiana Gutierrez is a recurring nurse character in The Evil Within survival horror video game series, serving as a mysterious guide and save-point attendant for the protagonist within the STEM world.
  • E. Michelle Navarro
    Michelle Navarro is an individual notable enough to be recognized as a prominent bearer of the Navarro surname.
  • 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: Rocsi Diaz
Triple: [Terrence J, coHostWith, Rocsi Diaz]
Generated description
Rocsi Diaz is an American television and radio personality best known as a former co-host of BET’s music video countdown show 106 & Park.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Rocsi Diaz
Target entity description: Rocsi Diaz is an American television and radio personality best known as a former co-host of BET’s music video countdown show 106 & Park.
  • A. Miluska Rosales
    Miluska Rosales is known as the spouse of Italian actor and television personality Rossano Rubicondi.
  • B. Silvia Navarro
    Silvia Navarro is a Mexican actress best known for her leading roles in popular telenovelas and television dramas.
  • C. Amada Cruz
    Amada Cruz is an American museum director and arts administrator known for leading major art institutions, including serving as director of the Seattle Art Museum.
  • D. Tatiana Gutierrez
    Tatiana Gutierrez is a recurring nurse character in The Evil Within survival horror video game series, serving as a mysterious guide and save-point attendant for the protagonist within the STEM world.
  • E. Michelle Navarro
    Michelle Navarro is an individual notable enough to be recognized as a prominent bearer of the Navarro surname.
  • 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_69ad8585d7988190af37365331093ccd completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69ada66da23c81908f063b44b48b1e53 completed March 8, 2026, 4:40 p.m.
NED1 Entity disambiguation (via context triple) batch_69b2e81e6f98819099317f97f0c7f546 completed March 12, 2026, 4:21 p.m.
NEDg Description generation batch_69b2ec48bf888190a5eeac0aa2cc50c3 completed March 12, 2026, 4:39 p.m.
NED2 Entity disambiguation (via description) batch_69b2ecd134e88190933e52d7f1f5be2a completed March 12, 2026, 4:41 p.m.
Created at: March 8, 2026, 3:06 p.m.