Triple

T15884902
Position Surface form Disambiguated ID Type / Status
Subject Irina E385166 entity
Predicate hasVariant P455 FINISHED
Object Arina
Arina is a feminine given name used in various Slavic and other cultures, often considered a variant of names like Irina.
 E1181118 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: Arina | Statement: [Irina, hasVariant, Arina]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Arina
Context triple: [Irina, hasVariant, Arina]
  • A. Kaiya
    Kaiya is a feminine given name used in various cultures, often associated with meanings related to the sea, forgiveness, or purity.
  • B. Sanae
    Sanae is a Japanese feminine given name borne by various notable figures in politics, entertainment, and other fields.
  • C. Reona
    Reona is the Japanese given name of Nobel Prize–winning physicist Leo Esaki, known for his pioneering work on quantum tunneling and semiconductor devices.
  • D. Kiana
    Kiana is a feminine given name used in various cultures, often considered a modern variant of names like Kiana or Kianna.
  • E. Kalina
    Kalina is a locality in Mumbai, India, known for its residential areas, educational institutions, and proximity to major commercial and transport hubs.
  • 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: Arina
Triple: [Irina, hasVariant, Arina]
Generated description
Arina is a feminine given name used in various Slavic and other cultures, often considered a variant of names like Irina.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Arina
Target entity description: Arina is a feminine given name used in various Slavic and other cultures, often considered a variant of names like Irina.
  • A. Kaiya
    Kaiya is a feminine given name used in various cultures, often associated with meanings related to the sea, forgiveness, or purity.
  • B. Sanae
    Sanae is a Japanese feminine given name borne by various notable figures in politics, entertainment, and other fields.
  • C. Reona
    Reona is the Japanese given name of Nobel Prize–winning physicist Leo Esaki, known for his pioneering work on quantum tunneling and semiconductor devices.
  • D. Kiana
    Kiana is a feminine given name used in various cultures, often considered a modern variant of names like Kiana or Kianna.
  • E. Kalina
    Kalina is a locality in Mumbai, India, known for its residential areas, educational institutions, and proximity to major commercial and transport hubs.
  • 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_69d86da5b800819083a31be937d738b0 completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e1561997bc8190a40e7d68defbbddd completed April 16, 2026, 9:35 p.m.
NED1 Entity disambiguation (via context triple) batch_69ffa95651f88190a9aac72a667b999f completed May 9, 2026, 9:38 p.m.
NEDg Description generation batch_69ffaa07df788190bae67f3d9a800331 completed May 9, 2026, 9:41 p.m.
NED2 Entity disambiguation (via description) batch_69ffaaa92a648190a09829ef3197223c completed May 9, 2026, 9:44 p.m.
Created at: April 10, 2026, 4:51 a.m.