Triple

T5591063
Position Surface form Disambiguated ID Type / Status
Subject María Nicolasa de Valdés y de la Carrera E146877 entity
Predicate givenName P17 FINISHED
Object María
María is a feminine given name of Hebrew origin, widely used in Spanish-speaking countries and associated with numerous historical and religious figures.
 E542342 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: María | Statement: [María Nicolasa de Valdés y de la Carrera, givenName, María]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: María
Context triple: [María Nicolasa de Valdés y de la Carrera, givenName, María]
  • A. María chosen
    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.
  • B. María
    "María" is a film featuring actress Taryn Power in a significant role.
  • C. María
    María is the given first name of Josefa Ortiz de Domínguez, a prominent figure in Mexico’s War of Independence.
  • D. Luisa
    Luisa is a feminine given name used in various languages, particularly Romance languages, as a form of the name Louise.
  • E. Francisca
    Francisca is a feminine given name, used in various European and Latin American cultures, that is cognate with the English name Frances.
  • F. None of above.
  • 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: María
Triple: [María Nicolasa de Valdés y de la Carrera, givenName, María]
Generated description
María is a feminine given name of Hebrew origin, widely used in Spanish-speaking countries and associated with numerous historical and religious figures.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: María
Target entity description: María is a feminine given name of Hebrew origin, widely used in Spanish-speaking countries and associated with numerous historical and religious figures.
  • A. 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.
  • B. María
    "María" is a film featuring actress Taryn Power in a significant role.
  • C. María
    María is the given first name of Josefa Ortiz de Domínguez, a prominent figure in Mexico’s War of Independence.
  • D. Luisa
    Luisa is a feminine given name used in various languages, particularly Romance languages, as a form of the name Louise.
  • E. Francisca
    Francisca is a feminine given name, used in various European and Latin American cultures, that is cognate with the English name Frances.
  • 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_69c009036c408190981a8d690b679b67 completed March 22, 2026, 3:21 p.m.
NER Named-entity recognition batch_69c020a1d4cc8190a52264dfba6aa011 completed March 22, 2026, 5:02 p.m.
NED1 Entity disambiguation (via context triple) batch_69c05a00985881909d441cfe05cb6afe completed March 22, 2026, 9:07 p.m.
NEDg Description generation batch_69c05d8890148190a4f81b2c1ca70886 completed March 22, 2026, 9:22 p.m.
NED2 Entity disambiguation (via description) batch_69c06209c3588190a6ededf9c198d5c5 completed March 22, 2026, 9:41 p.m.
Created at: March 22, 2026, 3:38 p.m.