Triple

T6888760
Position Surface form Disambiguated ID Type / Status
Subject Xenia E158990 entity
Predicate hasVariant P455 FINISHED
Object Ksenia
Ksenia is a feminine given name, commonly used in Slavic countries and derived from the Greek name Xenia, meaning "hospitality" or "guest-friendship."
 E627620 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: Ksenia | Statement: [Xenia, hasVariant, Ksenia]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Ksenia
Context triple: [Xenia, hasVariant, Ksenia]
  • A. Oksana
    Oksana is a feminine given name of Ukrainian origin, most famously borne by Olympic champion figure skater Oksana Baiul.
  • B. Yelena
    Yelena is a feminine given name of Slavic origin, commonly used in Russian-speaking countries and equivalent to Helen or Helena in English.
  • C. Nadya
    Nadya is a feminine given name, often used as a diminutive of Nadezhda in Slavic cultures.
  • D. Xenia Shestova
    Xenia Shestova was a Russian noblewoman and influential matriarch of the early Romanov dynasty, best known as the mother of Tsar Mikhail I of Russia.
  • E. Natalya
    Natalya is a feminine given name of Slavic origin, commonly used in Russian-speaking countries and derived from the Latin name Natalia.
  • 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: Ksenia
Triple: [Xenia, hasVariant, Ksenia]
Generated description
Ksenia is a feminine given name, commonly used in Slavic countries and derived from the Greek name Xenia, meaning "hospitality" or "guest-friendship."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Ksenia
Target entity description: Ksenia is a feminine given name, commonly used in Slavic countries and derived from the Greek name Xenia, meaning "hospitality" or "guest-friendship."
  • A. Oksana
    Oksana is a feminine given name of Ukrainian origin, most famously borne by Olympic champion figure skater Oksana Baiul.
  • B. Yelena
    Yelena is a feminine given name of Slavic origin, commonly used in Russian-speaking countries and equivalent to Helen or Helena in English.
  • C. Nadya
    Nadya is a feminine given name, often used as a diminutive of Nadezhda in Slavic cultures.
  • D. Xenia Shestova
    Xenia Shestova was a Russian noblewoman and influential matriarch of the early Romanov dynasty, best known as the mother of Tsar Mikhail I of Russia.
  • E. Natalya
    Natalya is a feminine given name of Slavic origin, commonly used in Russian-speaking countries and derived from the Latin name Natalia.
  • 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_69c6883568c8819081db6407e892cccc completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6d9101e30819084695ba0003a255c completed March 27, 2026, 7:22 p.m.
NED1 Entity disambiguation (via context triple) batch_69c748cc2f908190b593cd82133a7b16 completed March 28, 2026, 3:19 a.m.
NEDg Description generation batch_69c749d4b088819095f991f976592d04 completed March 28, 2026, 3:24 a.m.
NED2 Entity disambiguation (via description) batch_69c74aab12988190bd23cfcc06c55cde completed March 28, 2026, 3:27 a.m.
Created at: March 27, 2026, 2:23 p.m.