Triple

T1011241
Position Surface form Disambiguated ID Type / Status
Subject Central Transdanubia E21827 entity
Predicate contains P35 FINISHED
Object Veszprém
Veszprém is a historic city in western Hungary known for its medieval castle district and role as a regional cultural and administrative center.
 E168418 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: Veszprém | Statement: [Central Transdanubia, contains, Veszprém]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Veszprém
Context triple: [Central Transdanubia, contains, Veszprém]
  • A. Sopron
    Sopron is a historic city in western Hungary near the Austrian border, known for its well-preserved medieval old town and wine-making traditions.
  • B. Pécs
    Pécs is a historic cultural and university city in southwestern Hungary, renowned for its Roman and Ottoman heritage and its designation as a European Capital of Culture in 2010.
  • C. Pozsony
    Pozsony is the historical Hungarian name for the city now known as Bratislava, the capital of Slovakia.
  • D. Székesfehérvár
    Székesfehérvár is a historic city in central Hungary that served as a medieval royal seat and coronation site for Hungarian kings.
  • E. Kecskemét
    Kecskemét is a city in central Hungary known for its Art Nouveau architecture, cultural institutions, and role as an administrative and economic center of the region.
  • 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: Veszprém
Triple: [Central Transdanubia, contains, Veszprém]
Generated description
Veszprém is a historic city in western Hungary known for its medieval castle district and role as a regional cultural and administrative center.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Veszprém
Target entity description: Veszprém is a historic city in western Hungary known for its medieval castle district and role as a regional cultural and administrative center.
  • A. Sopron
    Sopron is a historic city in western Hungary near the Austrian border, known for its well-preserved medieval old town and wine-making traditions.
  • B. Pécs
    Pécs is a historic cultural and university city in southwestern Hungary, renowned for its Roman and Ottoman heritage and its designation as a European Capital of Culture in 2010.
  • C. Pozsony
    Pozsony is the historical Hungarian name for the city now known as Bratislava, the capital of Slovakia.
  • D. Székesfehérvár
    Székesfehérvár is a historic city in central Hungary that served as a medieval royal seat and coronation site for Hungarian kings.
  • E. Kecskemét
    Kecskemét is a city in central Hungary known for its Art Nouveau architecture, cultural institutions, and role as an administrative and economic center of the region.
  • 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_69a493c68e24819080ed0ee8bcfd5ce0 completed March 1, 2026, 7:30 p.m.
NER Named-entity recognition batch_69a4b7a5651081909f16a5fadd3992a4 completed March 1, 2026, 10:03 p.m.
NED1 Entity disambiguation (via context triple) batch_69ad15899808819090f6e26a40f7b2aa completed March 8, 2026, 6:22 a.m.
NEDg Description generation batch_69ad160dd8ec8190a3c1cab5158c3e74 completed March 8, 2026, 6:24 a.m.
NED2 Entity disambiguation (via description) batch_69ad1672eea08190a37c99aedf012fd5 completed March 8, 2026, 6:25 a.m.
Created at: March 1, 2026, 7:41 p.m.