Triple

T12870119
Position Surface form Disambiguated ID Type / Status
Subject La verdad E307825 entity
Predicate hasCharacter P2308 FINISHED
Object Lidia
Lidia is a central character in the Spanish television series "La verdad," around whom much of the mystery and drama of the plot revolves.
 E1006340 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: Lidia | Statement: [La verdad, hasCharacter, Lidia]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Lidia
Context triple: [La verdad, hasCharacter, Lidia]
  • A. Lidia
    Lidia Zamenhof was a Polish Esperantist, translator, and writer, known for promoting Esperanto and translating major literary works into the language.
  • B. Aleida
    Aleida is a feminine given name of Spanish origin, notably borne by Aleida Guevara, the daughter of revolutionary leader Che Guevara.
  • C. Luisa
    Luisa is a feminine given name used in various languages, particularly Romance languages, as a form of the name Louise.
  • D. Marcela
    Marcela is one of the given names of Alexia Juliana Marcela Laurentien, a member of the Dutch royal family.
  • E. Marta
    Marta is a feminine given name commonly used in many European and Latin American countries, often considered a variant of the name Martha.
  • 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: Lidia
Triple: [La verdad, hasCharacter, Lidia]
Generated description
Lidia is a central character in the Spanish television series "La verdad," around whom much of the mystery and drama of the plot revolves.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Lidia
Target entity description: Lidia is a central character in the Spanish television series "La verdad," around whom much of the mystery and drama of the plot revolves.
  • A. Lidia
    Lidia Zamenhof was a Polish Esperantist, translator, and writer, known for promoting Esperanto and translating major literary works into the language.
  • B. Aleida
    Aleida is a feminine given name of Spanish origin, notably borne by Aleida Guevara, the daughter of revolutionary leader Che Guevara.
  • C. Luisa
    Luisa is a feminine given name used in various languages, particularly Romance languages, as a form of the name Louise.
  • D. Marcela
    Marcela is one of the given names of Alexia Juliana Marcela Laurentien, a member of the Dutch royal family.
  • E. Marta
    Marta is a feminine given name commonly used in many European and Latin American countries, often considered a variant of the name Martha.
  • 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_69d7bdf69bc48190af6c2621f28ca351 completed April 9, 2026, 2:55 p.m.
NER Named-entity recognition batch_69d970905784819091631161a9de98c5 completed April 10, 2026, 9:50 p.m.
NED1 Entity disambiguation (via context triple) batch_69f69bb4b28c8190a4ec9cad4e1e0f05 completed May 3, 2026, 12:49 a.m.
NEDg Description generation batch_69f69cc60c488190a5a71e25c075e9ff completed May 3, 2026, 12:54 a.m.
NED2 Entity disambiguation (via description) batch_69f69d845a9081909b40562825c1c500 completed May 3, 2026, 12:57 a.m.
Created at: April 9, 2026, 5:38 p.m.