Triple

T3892842
Position Surface form Disambiguated ID Type / Status
Subject Agnes E88099 entity
Predicate hasVariant P455 FINISHED
Object Inês
Inês is a Portuguese given name, equivalent to Agnes, commonly used for women in Portuguese-speaking countries.
 E396508 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: Inês | Statement: [Agnes, hasVariant, Inês]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Inês
Context triple: [Agnes, hasVariant, Inês]
  • A. Inês de Castro
    Inês de Castro is a tragic figure from 14th-century Portuguese history and legend, famed as the posthumously recognized queen whose love affair with King Pedro I inspired numerous works of art and literature.
  • B. Fernanda
    Fernanda is a feminine given name commonly used in Romance-language countries, derived from the masculine name Ferdinand.
  • C. Francisca
    Francisca is a feminine given name, used in various European and Latin American cultures, that is cognate with the English name Frances.
  • D. Luisa de Guzmán
    Luisa de Guzmán was a Spanish-born noblewoman who became Queen Consort of Portugal and a key political figure in the Portuguese Restoration, as well as the mother of Catherine of Braganza.
  • E. Pilar
    Pilar is a strong-willed, perceptive Spanish guerrilla fighter who plays a central role in Ernest Hemingway’s novel "For Whom the Bell Tolls."
  • 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: Inês
Triple: [Agnes, hasVariant, Inês]
Generated description
Inês is a Portuguese given name, equivalent to Agnes, commonly used for women in Portuguese-speaking countries.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Inês
Target entity description: Inês is a Portuguese given name, equivalent to Agnes, commonly used for women in Portuguese-speaking countries.
  • A. Inês de Castro
    Inês de Castro is a tragic figure from 14th-century Portuguese history and legend, famed as the posthumously recognized queen whose love affair with King Pedro I inspired numerous works of art and literature.
  • B. Fernanda
    Fernanda is a feminine given name commonly used in Romance-language countries, derived from the masculine name Ferdinand.
  • C. Francisca
    Francisca is a feminine given name, used in various European and Latin American cultures, that is cognate with the English name Frances.
  • D. Luisa de Guzmán
    Luisa de Guzmán was a Spanish-born noblewoman who became Queen Consort of Portugal and a key political figure in the Portuguese Restoration, as well as the mother of Catherine of Braganza.
  • E. Pilar
    Pilar is a strong-willed, perceptive Spanish guerrilla fighter who plays a central role in Ernest Hemingway’s novel "For Whom the Bell Tolls."
  • 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_69aed9466d548190939f5217a23ed4ac completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aeecce860c8190b16eca2e14f6544f completed March 9, 2026, 3:52 p.m.
NED1 Entity disambiguation (via context triple) batch_69b51c96ff648190b03807547930d51d completed March 14, 2026, 8:30 a.m.
NEDg Description generation batch_69b51d14269881909b75083e3928163a completed March 14, 2026, 8:32 a.m.
NED2 Entity disambiguation (via description) batch_69b51d73ecd48190881c15ff6e0daea0 completed March 14, 2026, 8:33 a.m.
Created at: March 9, 2026, 3:21 p.m.