Triple

T1652354
Position Surface form Disambiguated ID Type / Status
Subject Göppingen E35718 entity
Predicate hasTwinTown P919 FINISHED
Object Sümeg
Sümeg is a small historic town in western Hungary, best known for its well-preserved medieval hilltop castle and baroque architecture.
 E186670 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: Sümeg | Statement: [Göppingen, hasTwinTown, Sümeg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Sümeg
Context triple: [Göppingen, hasTwinTown, Sümeg]
  • A. Zamárdi
    Zamárdi is a popular Hungarian resort town on the southern shore of Lake Balaton, known for its beaches, lakeside recreation, and summer festivals.
  • B. Komló
    Komló is a town in southern Hungary known historically for its coal mining and hop-growing industries.
  • C. Sarolt
    Sarolt was a prominent 10th-century Hungarian noblewoman and duchess, influential in the Christianization and early state formation of Hungary as the wife of Grand Prince Géza and mother of King Stephen I.
  • D. Tatabánya
    Tatabánya is an industrial city in northwestern Hungary known for its mining heritage and role as a regional economic center.
  • E. Keszthely
    Keszthely is a historic town in western Hungary known for its lakeside resort atmosphere, cultural heritage, and proximity to Lake Balaton.
  • 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: Sümeg
Triple: [Göppingen, hasTwinTown, Sümeg]
Generated description
Sümeg is a small historic town in western Hungary, best known for its well-preserved medieval hilltop castle and baroque architecture.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Sümeg
Target entity description: Sümeg is a small historic town in western Hungary, best known for its well-preserved medieval hilltop castle and baroque architecture.
  • A. Zamárdi
    Zamárdi is a popular Hungarian resort town on the southern shore of Lake Balaton, known for its beaches, lakeside recreation, and summer festivals.
  • B. Komló
    Komló is a town in southern Hungary known historically for its coal mining and hop-growing industries.
  • C. Sarolt
    Sarolt was a prominent 10th-century Hungarian noblewoman and duchess, influential in the Christianization and early state formation of Hungary as the wife of Grand Prince Géza and mother of King Stephen I.
  • D. Tatabánya
    Tatabánya is an industrial city in northwestern Hungary known for its mining heritage and role as a regional economic center.
  • E. Keszthely
    Keszthely is a historic town in western Hungary known for its lakeside resort atmosphere, cultural heritage, and proximity to Lake Balaton.
  • 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_69a8860568888190a32cd9f70acbba42 completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69a90a88cb108190a836b972f600c257 completed March 5, 2026, 4:46 a.m.
NED1 Entity disambiguation (via context triple) batch_69ad60ac133881909222b25029096407 completed March 8, 2026, 11:42 a.m.
NEDg Description generation batch_69ad613d218081909e3ed7e74dcf82fc completed March 8, 2026, 11:45 a.m.
NED2 Entity disambiguation (via description) batch_69ad61b178148190a088495eb599c9cb completed March 8, 2026, 11:46 a.m.
Created at: March 4, 2026, 7:29 p.m.