Triple

T2490651
Position Surface form Disambiguated ID Type / Status
Subject Lily E52031 entity
Predicate hasVariant P455 FINISHED
Object Liliana
Liliana is a feminine given name, often considered a more elaborate or romantic variant of Lily, used in various cultures around the world.
 E272364 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: Liliana | Statement: [Lily, hasVariant, Liliana]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Liliana
Context triple: [Lily, hasVariant, Liliana]
  • A. Luciana
    Luciana is a feminine given name of Latin origin, commonly used in Spanish- and Portuguese-speaking countries.
  • B. Lina
    Lina is a Native American servant in Toni Morrison’s novel *A Mercy*, whose history of displacement and resilience reflects the novel’s themes of slavery, colonialism, and survival in 17th-century America.
  • C. Lillita
    Lillita is the birth name of Lita Grey, the American actress best known for her early silent film work and marriage to Charlie Chaplin.
  • D. Lizella
    Lizella is an unincorporated community in central Georgia, United States, located near the city of Macon.
  • E. Marisa
    Marisa is a feminine given name of Latin origin, commonly used in Spanish- and Italian-speaking cultures.
  • 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: Liliana
Triple: [Lily, hasVariant, Liliana]
Generated description
Liliana is a feminine given name, often considered a more elaborate or romantic variant of Lily, used in various cultures around the world.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Liliana
Target entity description: Liliana is a feminine given name, often considered a more elaborate or romantic variant of Lily, used in various cultures around the world.
  • A. Luciana
    Luciana is a feminine given name of Latin origin, commonly used in Spanish- and Portuguese-speaking countries.
  • B. Lina
    Lina is a Native American servant in Toni Morrison’s novel *A Mercy*, whose history of displacement and resilience reflects the novel’s themes of slavery, colonialism, and survival in 17th-century America.
  • C. Lillita
    Lillita is the birth name of Lita Grey, the American actress best known for her early silent film work and marriage to Charlie Chaplin.
  • D. Lizella
    Lizella is an unincorporated community in central Georgia, United States, located near the city of Macon.
  • E. Marisa
    Marisa is a feminine given name of Latin origin, commonly used in Spanish- and Italian-speaking cultures.
  • 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_69ab4955111c8190835bf619adec21ff completed March 6, 2026, 9:38 p.m.
NER Named-entity recognition batch_69abd18fe32081909580c6272a6013c5 completed March 7, 2026, 7:19 a.m.
NED1 Entity disambiguation (via context triple) batch_69af1f9111ec8190b464da14bc4be11e completed March 9, 2026, 7:29 p.m.
NEDg Description generation batch_69af209cb5dc8190b0a9870462205cd2 completed March 9, 2026, 7:33 p.m.
NED2 Entity disambiguation (via description) batch_69af2119cf10819083e0c66ae63eed2c completed March 9, 2026, 7:35 p.m.
Created at: March 6, 2026, 9:45 p.m.