Triple

T2094743
Position Surface form Disambiguated ID Type / Status
Subject Fejér County E32753 entity
Predicate containsTown P847 FINISHED
Object Bicske
Bicske is a small town in central Hungary known for its historical significance and location along major transportation routes west of Budapest.
 E236811 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: Bicske | Statement: [Fejér County, containsTown, Bicske]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Bicske
Context triple: [Fejér County, containsTown, Bicske]
  • A. Tatabánya
    Tatabánya is an industrial city in northwestern Hungary known for its mining heritage and role as a regional economic center.
  • B. Bácsborsód
    Bácsborsód is a village in southern Hungary, notable as the birthplace of the influential Bauhaus artist and photographer László Moholy-Nagy.
  • C. Keszthely
    Keszthely is a historic town in western Hungary known for its lakeside resort atmosphere, cultural heritage, and proximity to Lake Balaton.
  • D. Sajó
    Sajó is a river in Central Europe that flows through Slovakia and northeastern Hungary before joining the Tisza River.
  • E. Komló
    Komló is a town in southern Hungary known historically for its coal mining and hop-growing industries.
  • 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: Bicske
Triple: [Fejér County, containsTown, Bicske]
Generated description
Bicske is a small town in central Hungary known for its historical significance and location along major transportation routes west of Budapest.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Bicske
Target entity description: Bicske is a small town in central Hungary known for its historical significance and location along major transportation routes west of Budapest.
  • A. Tatabánya
    Tatabánya is an industrial city in northwestern Hungary known for its mining heritage and role as a regional economic center.
  • B. Bácsborsód
    Bácsborsód is a village in southern Hungary, notable as the birthplace of the influential Bauhaus artist and photographer László Moholy-Nagy.
  • C. Keszthely
    Keszthely is a historic town in western Hungary known for its lakeside resort atmosphere, cultural heritage, and proximity to Lake Balaton.
  • D. Sajó
    Sajó is a river in Central Europe that flows through Slovakia and northeastern Hungary before joining the Tisza River.
  • E. Komló
    Komló is a town in southern Hungary known historically for its coal mining and hop-growing industries.
  • 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_69a885eba0708190999696a45cbec816 completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69abba99ddc48190bb2097b56efb7aca completed March 7, 2026, 5:41 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae518d29d4819090aa2a1c6fc7304d completed March 9, 2026, 4:50 a.m.
NEDg Description generation batch_69ae522f0394819087a7e7d9c6ca354c completed March 9, 2026, 4:53 a.m.
NED2 Entity disambiguation (via description) batch_69ae5316acf881908dde9d83c36c8fd0 completed March 9, 2026, 4:56 a.m.
Created at: March 4, 2026, 7:43 p.m.