Triple

T10005625
Position Surface form Disambiguated ID Type / Status
Subject Elle Brazil E198233 entity
Predicate publisher P29 FINISHED
Object Lola Editorial
Lola Editorial is a Brazilian publishing company best known for producing fashion and lifestyle magazines, including the Brazilian edition of Elle.
 E836060 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: Lola Editorial | Statement: [Elle Brazil, publisher, Lola Editorial]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Lola Editorial
Context triple: [Elle Brazil, publisher, Lola Editorial]
  • A. Lola
    Lola is a lethal, acrobatic henchwoman and primary antagonist in the action film "Transporter 2," known for her distinctive red attire and high-impact fight scenes.
  • B. Lola
    Lola is a 1981 West German drama film directed by Rainer Werner Fassbinder, in which Armin Mueller-Stahl plays a prominent role in a story set in postwar Germany.
  • C. Lola
    Lola is a 1961 French New Wave film directed by Jacques Demy, featuring Corinne Marchand in the title role as a cabaret singer in the port city of Nantes.
  • D. Lola
    Lola is the charismatic drag queen and performer who serves as the central catalyst for change in the musical and film "Kinky Boots."
  • E. Lola
    Lola is a fictional character portrayed by British actor Chiwetel Ejiofor.
  • 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: Lola Editorial
Triple: [Elle Brazil, publisher, Lola Editorial]
Generated description
Lola Editorial is a Brazilian publishing company best known for producing fashion and lifestyle magazines, including the Brazilian edition of Elle.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Lola Editorial
Target entity description: Lola Editorial is a Brazilian publishing company best known for producing fashion and lifestyle magazines, including the Brazilian edition of Elle.
  • A. Lola
    Lola is a lethal, acrobatic henchwoman and primary antagonist in the action film "Transporter 2," known for her distinctive red attire and high-impact fight scenes.
  • B. Lola
    Lola is a 1981 West German drama film directed by Rainer Werner Fassbinder, in which Armin Mueller-Stahl plays a prominent role in a story set in postwar Germany.
  • C. Lola
    Lola is a 1961 French New Wave film directed by Jacques Demy, featuring Corinne Marchand in the title role as a cabaret singer in the port city of Nantes.
  • D. Lola
    Lola is the charismatic drag queen and performer who serves as the central catalyst for change in the musical and film "Kinky Boots."
  • E. Lola
    Lola is a fictional character portrayed by British actor Chiwetel Ejiofor.
  • 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_69ca830fcca48190bbbd9b20c233835f completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cdcd157d9c8190b863e4264f9a48b1 completed April 2, 2026, 1:57 a.m.
NED1 Entity disambiguation (via context triple) batch_69d26a5d7e088190b5b1a852bfdc9073 completed April 5, 2026, 1:57 p.m.
NEDg Description generation batch_69d26ce0394c8190b8ea0c2ce125ef1b completed April 5, 2026, 2:08 p.m.
NED2 Entity disambiguation (via description) batch_69d270ad815481909806533bc8480c09 completed April 5, 2026, 2:24 p.m.
Created at: March 30, 2026, 8:51 p.m.