Triple

T3892841
Position Surface form Disambiguated ID Type / Status
Subject Agnes E88099 entity
Predicate hasVariant P455 FINISHED
Object Inés
Inés is a feminine given name, especially common in Spanish-speaking countries, derived from the name Agnes.
 E398718 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: Inés | Statement: [Agnes, hasVariant, Inés]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Inés
Context triple: [Agnes, hasVariant, Inés]
  • A. 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.
  • 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 a coastal municipality in the Philippine province of Bataan known for its historical significance in World War II and its role in the defense of Bataan.
  • E. Fernanda
    Fernanda is a feminine given name commonly used in Romance-language countries, derived from the masculine name Ferdinand.
  • 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: Inés
Triple: [Agnes, hasVariant, Inés]
Generated description
Inés is a feminine given name, especially common in Spanish-speaking countries, derived from the name Agnes.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Inés
Target entity description: Inés is a feminine given name, especially common in Spanish-speaking countries, derived from the name Agnes.
  • A. 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.
  • 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 a coastal municipality in the Philippine province of Bataan known for its historical significance in World War II and its role in the defense of Bataan.
  • E. Fernanda
    Fernanda is a feminine given name commonly used in Romance-language countries, derived from the masculine name Ferdinand.
  • 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_69b5284cf70481909e4efa1baf1b8815 completed March 14, 2026, 9:20 a.m.
NEDg Description generation batch_69b528eb52048190a1f1d97db958f70a completed March 14, 2026, 9:22 a.m.
NED2 Entity disambiguation (via description) batch_69b529938d188190ab9bc1794ca062f3 completed March 14, 2026, 9:25 a.m.
Created at: March 9, 2026, 3:21 p.m.