Triple

T612755
Position Surface form Disambiguated ID Type / Status
Subject Vojtech Tuka E12135 entity
Predicate familyName P18 FINISHED
Object Tuka
Tuka is a surname most notably associated with Vojtech Tuka, a Slovak politician and leading figure of the World War II-era Slovak State.
 E83894 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: Tuka | Statement: [Vojtech Tuka, familyName, Tuka]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Tuka
Context triple: [Vojtech Tuka, familyName, Tuka]
  • A. Dongo
    Dongo is a small town on the northwestern shore of Lake Como in Lombardy, Italy, known for its role in the capture of Benito Mussolini at the end of World War II.
  • B. Pulaar
    Pulaar is a Fula language variety spoken primarily by the Fula (Fulani) people across West Africa, including in Mauritania, Senegal, and neighboring countries.
  • C. Mvezo
    Mvezo is a small rural village in South Africa’s Eastern Cape best known as the birthplace of Nelson Mandela.
  • D. Taree
    Taree is a regional town in New South Wales, Australia, situated on the Manning River and serving as a commercial and service hub for the surrounding agricultural and coastal communities.
  • E. Tama
    Tama is a region in western Tokyo, Japan, encompassing several suburban cities and towns that serve as residential and commercial areas for the greater Tokyo metropolis.
  • 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: Tuka
Triple: [Vojtech Tuka, familyName, Tuka]
Generated description
Tuka is a surname most notably associated with Vojtech Tuka, a Slovak politician and leading figure of the World War II-era Slovak State.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Tuka
Target entity description: Tuka is a surname most notably associated with Vojtech Tuka, a Slovak politician and leading figure of the World War II-era Slovak State.
  • A. Dongo
    Dongo is a small town on the northwestern shore of Lake Como in Lombardy, Italy, known for its role in the capture of Benito Mussolini at the end of World War II.
  • B. Pulaar
    Pulaar is a Fula language variety spoken primarily by the Fula (Fulani) people across West Africa, including in Mauritania, Senegal, and neighboring countries.
  • C. Mvezo
    Mvezo is a small rural village in South Africa’s Eastern Cape best known as the birthplace of Nelson Mandela.
  • D. Taree
    Taree is a regional town in New South Wales, Australia, situated on the Manning River and serving as a commercial and service hub for the surrounding agricultural and coastal communities.
  • E. Tama
    Tama is a region in western Tokyo, Japan, encompassing several suburban cities and towns that serve as residential and commercial areas for the greater Tokyo metropolis.
  • 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_69a493309df48190a327f748e88049a6 completed March 1, 2026, 7:27 p.m.
NER Named-entity recognition batch_69a49e08dbf88190ab050078a63e266b completed March 1, 2026, 8:14 p.m.
NED1 Entity disambiguation (via context triple) batch_69a5dc8ff78c8190954d33f9e4556cca completed March 2, 2026, 6:53 p.m.
NEDg Description generation batch_69a5de26ff1081908a60b55a1deab804 completed March 2, 2026, 6:59 p.m.
NED2 Entity disambiguation (via description) batch_69a5ff1ac6f481909915fd5b2e648558 completed March 2, 2026, 9:20 p.m.
Created at: March 1, 2026, 7:35 p.m.