Triple

T10942131
Position Surface form Disambiguated ID Type / Status
Subject Beatriz Enríquez de Arana E258498 entity
Predicate givenName P17 FINISHED
Object Beatriz
Beatriz is a feminine given name of Spanish and Portuguese origin, equivalent to Beatrice in English.
 E560451 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: Beatriz | Statement: [Beatriz Enríquez de Arana, givenName, Beatriz]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Beatriz
Context triple: [Beatriz Enríquez de Arana, givenName, Beatriz]
  • A. Beatriz
    Beatriz is a surname most notably associated with actress Stephanie Beatriz, known for her role as Rosa Diaz on the television series "Brooklyn Nine-Nine."
  • B. 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."
  • C. Pilar
    Pilar is a riverside city in southwestern Paraguay known for its colonial architecture, river port activities, and proximity to the border with Argentina.
  • D. Pilar
    Pilar is the introspective female protagonist of Paulo Coelho’s novel "By the River Piedra I Sat Down and Wept," whose spiritual and emotional journey drives the story.
  • E. Pilar
    Pilar is a coastal town on Siargao Island in the Philippines, known for its fishing communities and access to popular surfing and eco-tourism spots.
  • 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: Beatriz
Triple: [Beatriz Enríquez de Arana, givenName, Beatriz]
Generated description
Beatriz is a feminine given name of Spanish and Portuguese origin, equivalent to Beatrice in English.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Beatriz
Target entity description: Beatriz is a feminine given name of Spanish and Portuguese origin, equivalent to Beatrice in English.
  • A. Beatriz chosen
    Beatriz is a surname most notably associated with actress Stephanie Beatriz, known for her role as Rosa Diaz on the television series "Brooklyn Nine-Nine."
  • B. Pilar
    Pilar is the introspective female protagonist of Paulo Coelho’s novel "By the River Piedra I Sat Down and Wept," whose spiritual and emotional journey drives the story.
  • C. Pilar
    Pilar is a coastal town on Siargao Island in the Philippines, known for its fishing communities and access to popular surfing and eco-tourism spots.
  • D. 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."
  • E. Pilar
    Pilar is a riverside city in southwestern Paraguay known for its colonial architecture, river port activities, and proximity to the border with Argentina.
  • 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_69d6aa8769b4819082bfe5e61b9017f0 completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d770c33a7c8190b3347944f68ee431 completed April 9, 2026, 9:26 a.m.
NED1 Entity disambiguation (via context triple) batch_69e23c1ed4f081909ac5731dac325466 completed April 17, 2026, 1:56 p.m.
NEDg Description generation batch_69e24542b4f081909c97621f04da8ecc completed April 17, 2026, 2:35 p.m.
NED2 Entity disambiguation (via description) batch_69e248f7f96481909fa6e6cd07891566 completed April 17, 2026, 2:51 p.m.
Created at: April 8, 2026, 9:23 p.m.