Triple

T2179937
Position Surface form Disambiguated ID Type / Status
Subject Martha E49016 entity
Predicate hasCognate P2525 FINISHED
Object Marta (Polish)
Marta is a common Polish female given name, equivalent to Martha, traditionally associated with Christian and European naming traditions.
 E241292 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: Marta (Polish) | Statement: [Martha, hasCognate, Marta (Polish)]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Marta (Polish)
Context triple: [Martha, hasCognate, Marta (Polish)]
  • A. Marta Helena Skowrońska
    Marta Helena Skowrońska, later known as Catherine I of Russia, was a former Lithuanian-born servant who rose to become Empress and autocratic ruler of the Russian Empire as the wife and successor of Peter the Great.
  • B. Ewelina Hańska
    Ewelina Hańska was a Polish noblewoman best known as the longtime correspondent, muse, and eventually wife of French novelist Honoré de Balzac.
  • C. Beata
    Beata is a feminine given name of Latin origin, commonly used in various European countries and meaning "blessed" or "happy."
  • D. Hanna Zdanowska
    Hanna Zdanowska is a Polish politician best known for serving as the long-time mayor of the city of Łódź.
  • E. Marta Kwiatkowska
    Marta Kwiatkowska is a prominent computer scientist known for her contributions to probabilistic model checking and formal verification.
  • 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: Marta (Polish)
Triple: [Martha, hasCognate, Marta (Polish)]
Generated description
Marta is a common Polish female given name, equivalent to Martha, traditionally associated with Christian and European naming traditions.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Marta (Polish)
Target entity description: Marta is a common Polish female given name, equivalent to Martha, traditionally associated with Christian and European naming traditions.
  • A. Marta Helena Skowrońska
    Marta Helena Skowrońska, later known as Catherine I of Russia, was a former Lithuanian-born servant who rose to become Empress and autocratic ruler of the Russian Empire as the wife and successor of Peter the Great.
  • B. Ewelina Hańska
    Ewelina Hańska was a Polish noblewoman best known as the longtime correspondent, muse, and eventually wife of French novelist Honoré de Balzac.
  • C. Beata
    Beata is a feminine given name of Latin origin, commonly used in various European countries and meaning "blessed" or "happy."
  • D. Hanna Zdanowska
    Hanna Zdanowska is a Polish politician best known for serving as the long-time mayor of the city of Łódź.
  • E. Marta Kwiatkowska
    Marta Kwiatkowska is a prominent computer scientist known for her contributions to probabilistic model checking and formal verification.
  • 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_69a88aa72d348190a9544bb5b8a4e71d completed March 4, 2026, 7:40 p.m.
NER Named-entity recognition batch_69abbef0e2f0819080ca457fe3b8b419 completed March 7, 2026, 6 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae5da5930c819087e71a609f76e269 completed March 9, 2026, 5:41 a.m.
NEDg Description generation batch_69ae5e4a45a08190bd96af6cda06ab35 completed March 9, 2026, 5:44 a.m.
NED2 Entity disambiguation (via description) batch_69ae5ec4a35c8190bffc7a183497e764 completed March 9, 2026, 5:46 a.m.
Created at: March 4, 2026, 7:45 p.m.