Triple

T1913034
Position Surface form Disambiguated ID Type / Status
Subject Miskolc E38152 entity
Predicate locatedNearRiver P165 FINISHED
Object Sajó
Sajó is a river in Central Europe that flows through Slovakia and northeastern Hungary before joining the Tisza River.
 E215781 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: Sajó | Statement: [Miskolc, locatedNearRiver, Sajó]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Sajó
Context triple: [Miskolc, locatedNearRiver, Sajó]
  • A. Bácsborsód
    Bácsborsód is a village in southern Hungary, notable as the birthplace of the influential Bauhaus artist and photographer László Moholy-Nagy.
  • B. Tatabánya
    Tatabánya is an industrial city in northwestern Hungary known for its mining heritage and role as a regional economic center.
  • C. Zamárdi
    Zamárdi is a popular Hungarian resort town on the southern shore of Lake Balaton, known for its beaches, lakeside recreation, and summer festivals.
  • D. 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.
  • 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: Sajó
Triple: [Miskolc, locatedNearRiver, Sajó]
Generated description
Sajó is a river in Central Europe that flows through Slovakia and northeastern Hungary before joining the Tisza River.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Sajó
Target entity description: Sajó is a river in Central Europe that flows through Slovakia and northeastern Hungary before joining the Tisza River.
  • A. Bácsborsód
    Bácsborsód is a village in southern Hungary, notable as the birthplace of the influential Bauhaus artist and photographer László Moholy-Nagy.
  • B. Tatabánya
    Tatabánya is an industrial city in northwestern Hungary known for its mining heritage and role as a regional economic center.
  • C. Zamárdi
    Zamárdi is a popular Hungarian resort town on the southern shore of Lake Balaton, known for its beaches, lakeside recreation, and summer festivals.
  • D. 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.
  • 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_69a8862a26088190aae5243695aeefc0 completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69abb1e26b948190aa194c30755ac5df completed March 7, 2026, 5:04 a.m.
NED1 Entity disambiguation (via context triple) batch_69adf3d5972881908856b75b324a1ad2 completed March 8, 2026, 10:10 p.m.
NEDg Description generation batch_69adf44290748190b882559de536af09 completed March 8, 2026, 10:12 p.m.
NED2 Entity disambiguation (via description) batch_69adf4d0ac58819096659706ef0785d0 completed March 8, 2026, 10:14 p.m.
Created at: March 4, 2026, 7:35 p.m.