Triple

T16766006
Position Surface form Disambiguated ID Type / Status
Subject Fertő-Hanság National Park E407463 entity
Predicate mainSettlementNearby P13187 FINISHED
Object Fertőd
Fertőd is a small town in northwestern Hungary best known for the grand Esterházy Palace, often called the “Hungarian Versailles.”
 E1263362 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: Fertőd | Statement: [Fertő-Hanság National Park, mainSettlementNearby, Fertőd]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Fertőd
Context triple: [Fertő-Hanság National Park, mainSettlementNearby, Fertőd]
  • A. Füzesabony
    Füzesabony is a small town in northeastern Hungary known as a regional railway junction and gateway to the Bükk and Mátra regions.
  • B. Dombóvár
    Dombóvár is a town in southern Hungary known as an important local transport and economic center within Tolna County.
  • C. Tiszaújváros
    Tiszaújváros is an industrial town in northeastern Hungary known for its large chemical and energy industries and its location along the Tisza River.
  • D. Hajdúdorog
    Hajdúdorog is a town in northeastern Hungary known as a center of the Hungarian Greek Catholic Church.
  • E. Dunakeszi
    Dunakeszi is a town in Hungary located just north of Budapest, known as a rapidly growing suburban and commuter settlement along the Danube in Pest County.
  • 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: Fertőd
Triple: [Fertő-Hanság National Park, mainSettlementNearby, Fertőd]
Generated description
Fertőd is a small town in northwestern Hungary best known for the grand Esterházy Palace, often called the “Hungarian Versailles.”
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Fertőd
Target entity description: Fertőd is a small town in northwestern Hungary best known for the grand Esterházy Palace, often called the “Hungarian Versailles.”
  • A. Füzesabony
    Füzesabony is a small town in northeastern Hungary known as a regional railway junction and gateway to the Bükk and Mátra regions.
  • B. Dombóvár
    Dombóvár is a town in southern Hungary known as an important local transport and economic center within Tolna County.
  • C. Tiszaújváros
    Tiszaújváros is an industrial town in northeastern Hungary known for its large chemical and energy industries and its location along the Tisza River.
  • D. Hajdúdorog
    Hajdúdorog is a town in northeastern Hungary known as a center of the Hungarian Greek Catholic Church.
  • E. Dunakeszi
    Dunakeszi is a town in Hungary located just north of Budapest, known as a rapidly growing suburban and commuter settlement along the Danube in Pest County.
  • 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_69d8839174188190909f190097207065 completed April 10, 2026, 4:58 a.m.
NER Named-entity recognition batch_69e3b0330d6081908ce99f14c70b90f2 completed April 18, 2026, 4:24 p.m.
NED1 Entity disambiguation (via context triple) batch_6a018c3489848190869bebedcb5c0564 completed May 11, 2026, 7:58 a.m.
NEDg Description generation batch_6a018da48b448190a088d9e537454817 completed May 11, 2026, 8:04 a.m.
NED2 Entity disambiguation (via description) batch_6a018e57fa708190872ad2fb5f660507 completed May 11, 2026, 8:07 a.m.
Created at: April 10, 2026, 5:21 a.m.