Triple

T3082989
Position Surface form Disambiguated ID Type / Status
Subject Maria Manuela, Princess of Portugal E64301 entity
Predicate givenName P17 FINISHED
Object Manuela
Manuela is the given name of Maria Manuela, a 16th-century Portuguese princess who became Queen of Castile through marriage to King Philip II of Spain.
 E325930 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: Manuela | Statement: [Maria Manuela, Princess of Portugal, givenName, Manuela]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Manuela
Context triple: [Maria Manuela, Princess of Portugal, givenName, Manuela]
  • A. Carmelina
    Carmelina is a lesser-known Broadway musical with music by Burton Lane and lyrics by Alan Jay Lerner, loosely based on the film "Buona Sera, Mrs. Campbell."
  • B. Luisa
    Luisa is a feminine given name used in various languages, particularly Romance languages, as a form of the name Louise.
  • C. María
    "María" is a film featuring actress Taryn Power in a significant role.
  • D. María
    María is a key character in Ernest Hemingway's novel "For Whom the Bell Tolls," known as a young Spanish woman and love interest of the protagonist amid the Spanish Civil War.
  • E. Josefa
    Josefa is a feminine given name of Spanish origin, historically borne by notable figures such as Mexican independence heroine Josefa Ortiz de Domínguez.
  • 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: Manuela
Triple: [Maria Manuela, Princess of Portugal, givenName, Manuela]
Generated description
Manuela is the given name of Maria Manuela, a 16th-century Portuguese princess who became Queen of Castile through marriage to King Philip II of Spain.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Manuela
Target entity description: Manuela is the given name of Maria Manuela, a 16th-century Portuguese princess who became Queen of Castile through marriage to King Philip II of Spain.
  • A. Carmelina
    Carmelina is a lesser-known Broadway musical with music by Burton Lane and lyrics by Alan Jay Lerner, loosely based on the film "Buona Sera, Mrs. Campbell."
  • B. Luisa
    Luisa is a feminine given name used in various languages, particularly Romance languages, as a form of the name Louise.
  • C. María
    "María" is a film featuring actress Taryn Power in a significant role.
  • D. María
    María is a key character in Ernest Hemingway's novel "For Whom the Bell Tolls," known as a young Spanish woman and love interest of the protagonist amid the Spanish Civil War.
  • E. Josefa
    Josefa is a feminine given name of Spanish origin, historically borne by notable figures such as Mexican independence heroine Josefa Ortiz de Domínguez.
  • 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_69ad857bb4c88190a4cf27893fcabed8 completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69ada1e877008190aacbd6f1357bdb9b completed March 8, 2026, 4:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69b1f89847e48190b82849701e119758 completed March 11, 2026, 11:19 p.m.
NEDg Description generation batch_69b1f9608e88819098f4044e54e0d908 completed March 11, 2026, 11:23 p.m.
NED2 Entity disambiguation (via description) batch_69b1fe3c8f408190988e7c7e3a51057e completed March 11, 2026, 11:43 p.m.
Created at: March 8, 2026, 3:03 p.m.