Triple

T19873832
Position Surface form Disambiguated ID Type / Status
Subject Graça Morais E477587 entity
Predicate givenName P17 FINISHED
Object Graça
Graça is a Portuguese given name commonly used for women, often associated with cultural and artistic figures in Portugal.
 E108959 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: Graça | Statement: [Graça Morais, givenName, Graça]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Graça
Context triple: [Graça Morais, givenName, Graça]
  • A. Graça
    Graça is a historic hilltop neighborhood in Lisbon, Portugal, known for its traditional streets, viewpoints over the city, and classic tram connections.
  • B. Graça
    Graça is a civil parish located within the municipality of Pedrógão Grande in central Portugal.
  • C. Rosário
    Rosário is a municipality in the Brazilian state of Maranhão, known for its regional culture and role in the state's interior.
  • D. Redenção
    Redenção is a municipality in the state of Ceará, Brazil, known historically as one of the first Brazilian cities to abolish slavery.
  • E. Gracia
    Gracia is a feminine given name, commonly used in Spanish and other Romance languages, that derives from and shares the meaning of the name Grace.
  • 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: Graça
Triple: [Graça Morais, givenName, Graça]
Generated description
Graça is a Portuguese given name commonly used for women, often associated with cultural and artistic figures in Portugal.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Graça
Target entity description: Graça is a Portuguese given name commonly used for women, often associated with cultural and artistic figures in Portugal.
  • A. Graça chosen
    Graça is a historic hilltop neighborhood in Lisbon, Portugal, known for its traditional streets, viewpoints over the city, and classic tram connections.
  • B. Graça
    Graça is a civil parish located within the municipality of Pedrógão Grande in central Portugal.
  • C. Rosário
    Rosário is a municipality in the Brazilian state of Maranhão, known for its regional culture and role in the state's interior.
  • D. Redenção
    Redenção is a municipality in the state of Ceará, Brazil, known historically as one of the first Brazilian cities to abolish slavery.
  • E. Gracia
    Gracia is a feminine given name, commonly used in Spanish and other Romance languages, that derives from and shares the meaning of the name Grace.
  • F. None of above.

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_69d8e51e7d948190aedbcd6c30361c39 completed April 10, 2026, 11:55 a.m.
NER Named-entity recognition batch_69e658d92f9c8190b363587ed1881c2c completed April 20, 2026, 4:48 p.m.
NED1 Entity disambiguation (via context triple) batch_6a07dbc653b481908e2884c1d2aa6ead completed May 16, 2026, 2:51 a.m.
NEDg Description generation batch_6a07dfbf3c5881909395e9cfa4986429 completed May 16, 2026, 3:08 a.m.
NED2 Entity disambiguation (via description) batch_6a07e08884788190b1c43d01e6022b4f completed May 16, 2026, 3:12 a.m.
Created at: April 10, 2026, 1:51 p.m.