Triple

T21598221
Position Surface form Disambiguated ID Type / Status
Subject Sazka Arena E532958 entity
Predicate namedAfter P63 FINISHED
Object Sazka
Sazka is a Czech lottery and betting company that has also been a prominent sponsor of major sports and entertainment venues.
 E1492415 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: Sazka | Statement: [Sazka Arena, namedAfter, Sazka]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Sazka
Context triple: [Sazka Arena, namedAfter, Sazka]
  • A. Žehra
    Žehra is a historic village in eastern Slovakia renowned for its UNESCO-listed Gothic church and its proximity to the medieval Spiš Castle.
  • B. Szerencs
    Szerencs is a small town in northeastern Hungary known for its historic castle and long-standing confectionery and wine-making traditions.
  • C. Samruk-Kazyna
    Samruk-Kazyna is Kazakhstan’s sovereign wealth fund, managing state-owned assets and strategic investments across key sectors of the national economy.
  • D. Sacchi
    Sacchi is an Italian surname most famously associated with Arrigo Sacchi, the influential former AC Milan and Italy national team football manager.
  • E. Le Tote
    Le Tote is a fashion rental subscription service company that expanded into traditional retail by acquiring the historic department store chain Lord & Taylor.
  • 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: Sazka
Triple: [Sazka Arena, namedAfter, Sazka]
Generated description
Sazka is a Czech lottery and betting company that has also been a prominent sponsor of major sports and entertainment venues.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Sazka
Target entity description: Sazka is a Czech lottery and betting company that has also been a prominent sponsor of major sports and entertainment venues.
  • A. Žehra
    Žehra is a historic village in eastern Slovakia renowned for its UNESCO-listed Gothic church and its proximity to the medieval Spiš Castle.
  • B. Szerencs
    Szerencs is a small town in northeastern Hungary known for its historic castle and long-standing confectionery and wine-making traditions.
  • C. Samruk-Kazyna
    Samruk-Kazyna is Kazakhstan’s sovereign wealth fund, managing state-owned assets and strategic investments across key sectors of the national economy.
  • D. Sacchi
    Sacchi is an Italian surname most famously associated with Arrigo Sacchi, the influential former AC Milan and Italy national team football manager.
  • E. Le Tote
    Le Tote is a fashion rental subscription service company that expanded into traditional retail by acquiring the historic department store chain Lord & Taylor.
  • 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_69e0c46364608190a337dc8720dc2a35 completed April 16, 2026, 11:13 a.m.
NER Named-entity recognition batch_69eefae2fc908190b989f39e8cefffb2 completed April 27, 2026, 5:57 a.m.
NED1 Entity disambiguation (via context triple) batch_6a09fd311894819091add0ade2c10c78 completed May 17, 2026, 5:38 p.m.
NEDg Description generation batch_6a09fe084a648190a37d701fb7f2f285 completed May 17, 2026, 5:42 p.m.
NED2 Entity disambiguation (via description) batch_6a09fecd7d688190b41ad68ff95ffab6 completed May 17, 2026, 5:45 p.m.
Created at: April 16, 2026, 6:32 p.m.