Triple

T1983441
Position Surface form Disambiguated ID Type / Status
Subject Kosovo E43081 entity
Predicate hasCity P316 FINISHED
Object Prizren
Prizren is a historic and culturally rich city in southern Kosovo, known for its well-preserved Ottoman-era architecture and diverse religious heritage.
 E231637 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: Prizren | Statement: [Kosovo, hasCity, Prizren]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Prizren
Context triple: [Kosovo, hasCity, Prizren]
  • A. Monastir
    Monastir, known today as Bitola in North Macedonia, is a historic Balkan city that played a significant strategic role during World War I.
  • B. Pristina
    Pristina is the capital and largest city of Kosovo, serving as its political, economic, and cultural center in the central Balkans.
  • C. Tirana
    Tirana is the capital and largest city of Albania, serving as its political, economic, and cultural center in the Balkans.
  • D. Skopje
    Skopje is the capital and largest city of North Macedonia, known for its historic Ottoman and Byzantine heritage alongside extensive modern redevelopment.
  • E. Nikšić
    Nikšić is one of the largest cities in Montenegro, known as an important industrial, cultural, and educational center of the country.
  • 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: Prizren
Triple: [Kosovo, hasCity, Prizren]
Generated description
Prizren is a historic and culturally rich city in southern Kosovo, known for its well-preserved Ottoman-era architecture and diverse religious heritage.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Prizren
Target entity description: Prizren is a historic and culturally rich city in southern Kosovo, known for its well-preserved Ottoman-era architecture and diverse religious heritage.
  • A. Monastir
    Monastir, known today as Bitola in North Macedonia, is a historic Balkan city that played a significant strategic role during World War I.
  • B. Pristina
    Pristina is the capital and largest city of Kosovo, serving as its political, economic, and cultural center in the central Balkans.
  • C. Tirana
    Tirana is the capital and largest city of Albania, serving as its political, economic, and cultural center in the Balkans.
  • D. Skopje
    Skopje is the capital and largest city of North Macedonia, known for its historic Ottoman and Byzantine heritage alongside extensive modern redevelopment.
  • E. Nikšić
    Nikšić is one of the largest cities in Montenegro, known as an important industrial, cultural, and educational center of the country.
  • 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_69a88713ddc88190a969715658ebe7a8 completed March 4, 2026, 7:25 p.m.
NER Named-entity recognition batch_69abb820815481908aac6d89b437225b completed March 7, 2026, 5:31 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae2705752c81908054e8e0e426e86d completed March 9, 2026, 1:48 a.m.
NEDg Description generation batch_69ae2901eb588190863e15deb8614754 completed March 9, 2026, 1:57 a.m.
NED2 Entity disambiguation (via description) batch_69ae297ebf6c819086e10ee455bea988 completed March 9, 2026, 1:59 a.m.
Created at: March 4, 2026, 7:37 p.m.