Triple

T9363385
Position Surface form Disambiguated ID Type / Status
Subject Magdolna Purgly E225333 entity
Predicate givenName P17 FINISHED
Object Magdolna
Magdolna is a Hungarian feminine given name, equivalent to Magdalene, traditionally associated with Christian and Central European naming customs.
 E200104 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: Magdolna | Statement: [Magdolna Purgly, givenName, Magdolna]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Magdolna
Context triple: [Magdolna Purgly, givenName, Magdolna]
  • A. Magda
    Magda is a feminine given name, commonly used as a short form of Magdalena in various European languages.
  • B. Rózsa
    Rózsa is the Hungarian given name of Rosika Schwimmer, a prominent early 20th-century feminist, pacifist, and suffragist activist.
  • C. Katalin
    Katalin is a Hungarian given name most prominently associated with biochemist Katalin Karikó, a pioneer of mRNA technology used in COVID-19 vaccines.
  • D. Margareta
    Margareta is a feminine given name used in various European languages, closely related to and derived from the name Margaret.
  • E. Mária
    Mária is the Hungarian and Slovak form of the given name Mary, commonly used in Central and Eastern Europe.
  • 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: Magdolna
Triple: [Magdolna Purgly, givenName, Magdolna]
Generated description
Magdolna is a Hungarian feminine given name, equivalent to Magdalene, traditionally associated with Christian and Central European naming customs.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Magdolna
Target entity description: Magdolna is a Hungarian feminine given name, equivalent to Magdalene, traditionally associated with Christian and Central European naming customs.
  • A. Magda chosen
    Magda is a feminine given name, commonly used as a short form of Magdalena in various European languages.
  • B. Rózsa
    Rózsa is the Hungarian given name of Rosika Schwimmer, a prominent early 20th-century feminist, pacifist, and suffragist activist.
  • C. Katalin
    Katalin is a Hungarian given name most prominently associated with biochemist Katalin Karikó, a pioneer of mRNA technology used in COVID-19 vaccines.
  • D. Margareta
    Margareta is a feminine given name used in various European languages, closely related to and derived from the name Margaret.
  • E. Mária
    Mária is the Hungarian and Slovak form of the given name Mary, commonly used in Central and Eastern Europe.
  • F. None of above.

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_69ca842bdd648190904131d58620d448 completed March 30, 2026, 2:09 p.m.
NER Named-entity recognition batch_69cd503ddf8c81908b090afa54ec5e6d completed April 1, 2026, 5:05 p.m.
NED1 Entity disambiguation (via context triple) batch_69d0f3eaf2208190967a3a8e82a4070b completed April 4, 2026, 11:20 a.m.
NEDg Description generation batch_69d0f5127c688190b2b383cd0714a6c3 completed April 4, 2026, 11:25 a.m.
NED2 Entity disambiguation (via description) batch_69d0f61dc2b88190920d754f55caa4ec completed April 4, 2026, 11:29 a.m.
Created at: March 30, 2026, 7:42 p.m.