Triple

T16690161
Position Surface form Disambiguated ID Type / Status
Subject Leonor Ernestina von Daun E405572 entity
Predicate givenName P17 FINISHED
Object Ernestina
Ernestina is a feminine given name of Germanic origin, historically borne by European nobility such as Leonor Ernestina von Daun.
 E1228694 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: Ernestina | Statement: [Leonor Ernestina von Daun, givenName, Ernestina]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Ernestina
Context triple: [Leonor Ernestina von Daun, givenName, Ernestina]
  • A. Fernanda
    Fernanda is a feminine given name commonly used in Romance-language countries, derived from the masculine name Ferdinand.
  • B. Eugênia
    Eugênia is a Portuguese given name, equivalent to Eugenia, commonly used in Brazil and other Lusophone countries.
  • C. Graciela
    Graciela is a feminine given name of Spanish origin, often considered a variant of Graziella and related to the concept of grace.
  • D. Encarnita
    Encarnita is the birth name of Puerto Rican singer-songwriter Kany García, known for her Latin pop and ballad music.
  • E. Marcela
    Marcela is one of the given names of Alexia Juliana Marcela Laurentien, a member of the Dutch royal family.
  • 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: Ernestina
Triple: [Leonor Ernestina von Daun, givenName, Ernestina]
Generated description
Ernestina is a feminine given name of Germanic origin, historically borne by European nobility such as Leonor Ernestina von Daun.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Ernestina
Target entity description: Ernestina is a feminine given name of Germanic origin, historically borne by European nobility such as Leonor Ernestina von Daun.
  • A. Fernanda
    Fernanda is a feminine given name commonly used in Romance-language countries, derived from the masculine name Ferdinand.
  • B. Eugênia
    Eugênia is a Portuguese given name, equivalent to Eugenia, commonly used in Brazil and other Lusophone countries.
  • C. Graciela
    Graciela is a feminine given name of Spanish origin, often considered a variant of Graziella and related to the concept of grace.
  • D. Encarnita
    Encarnita is the birth name of Puerto Rican singer-songwriter Kany García, known for her Latin pop and ballad music.
  • E. Marcela
    Marcela is one of the given names of Alexia Juliana Marcela Laurentien, a member of the Dutch royal family.
  • 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_69d8838c28748190b3f5967c743940ab completed April 10, 2026, 4:58 a.m.
NER Named-entity recognition batch_69e37ea8cabc8190ba321503399960da completed April 18, 2026, 12:52 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0091966fbc81908cd1db230ddbb82b completed May 10, 2026, 2:09 p.m.
NEDg Description generation batch_6a00920dd72c8190b30d4edd779e7029 completed May 10, 2026, 2:11 p.m.
NED2 Entity disambiguation (via description) batch_6a0092e322d08190862ae42a28c9e5cf completed May 10, 2026, 2:14 p.m.
Created at: April 10, 2026, 5:19 a.m.