Triple

T8463715
Position Surface form Disambiguated ID Type / Status
Subject Lena E200105 entity
Predicate hasVariant P455 FINISHED
Object Lenka
Lenka is a feminine given name, commonly used in Slavic countries, often as a diminutive or variant of names like Elena or Helena.
 E736056 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: Lenka | Statement: [Lena, hasVariant, Lenka]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Lenka
Context triple: [Lena, hasVariant, Lenka]
  • A. Libuše
    Libuše is a Czech opera by Bedřich Smetana, centered on the legendary princess Libuše who prophesies the glory of Prague and the Czech nation.
  • B. Lenka Peterson
    Lenka Peterson was an American stage, film, and television actress known for her versatile character roles from the mid-20th century onward.
  • C. Kája
    Kája is a Czech diminutive form of the given name Karel.
  • D. Mila
    Mila is a leading artificial intelligence research institute based in Quebec, renowned for its work in deep learning and machine learning.
  • E. Zhanna
    Zhanna is a feminine given name commonly used in Russian and other Slavic cultures, equivalent to Jeanne or Joanna.
  • 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: Lenka
Triple: [Lena, hasVariant, Lenka]
Generated description
Lenka is a feminine given name, commonly used in Slavic countries, often as a diminutive or variant of names like Elena or Helena.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Lenka
Target entity description: Lenka is a feminine given name, commonly used in Slavic countries, often as a diminutive or variant of names like Elena or Helena.
  • A. Libuše
    Libuše is a Czech opera by Bedřich Smetana, centered on the legendary princess Libuše who prophesies the glory of Prague and the Czech nation.
  • B. Lenka Peterson
    Lenka Peterson was an American stage, film, and television actress known for her versatile character roles from the mid-20th century onward.
  • C. Kája
    Kája is a Czech diminutive form of the given name Karel.
  • D. Mila
    Mila is a leading artificial intelligence research institute based in Quebec, renowned for its work in deep learning and machine learning.
  • E. Zhanna
    Zhanna is a feminine given name commonly used in Russian and other Slavic cultures, equivalent to Jeanne or Joanna.
  • 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_69ca83198c4c8190a337bf717d1813f5 completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cbe4a39bd48190b72be7e03cff323b completed March 31, 2026, 3:13 p.m.
NED1 Entity disambiguation (via context triple) batch_69ce39d5f50081908e273d5286a0d397 completed April 2, 2026, 9:41 a.m.
NEDg Description generation batch_69ce3bf7d2748190ad7ca0649fe2cb0f completed April 2, 2026, 9:50 a.m.
NED2 Entity disambiguation (via description) batch_69ce3c8aac8c8190a81c2c51cd0c06e0 completed April 2, 2026, 9:53 a.m.
Created at: March 30, 2026, 6:10 p.m.