Triple

T7819422
Position Surface form Disambiguated ID Type / Status
Subject Edvard Kardelj E181090 entity
Predicate workLocation P7 FINISHED
Object Belgrade E24551 NE FINISHED

How this triple was built (2 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: Belgrade | Statement: [Edvard Kardelj, workLocation, Belgrade]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Belgrade
Context triple: [Edvard Kardelj, workLocation, Belgrade]
  • A. Belgrade chosen
    Belgrade is the capital and largest city of Serbia, historically significant as a strategic crossroads between Central Europe and the Balkans on the confluence of the Sava and Danube rivers.
  • B. Novi Sad
    Novi Sad is Serbia’s second-largest city and the cultural and economic center of the northern Vojvodina region, known for its historic architecture and the EXIT music festival.
  • C. Kragujevac
    Kragujevac is a central Serbian city historically significant as an early capital and industrial and cultural hub of the country.
  • D. Bajina Bašta
    Bajina Bašta is a small town in western Serbia known for its scenic location on the Drina River and proximity to the Tara National Park.
  • E. Novi Beograd
    Novi Beograd is a large modern municipality of Belgrade known for its planned urban layout, wide boulevards, and extensive residential and business districts on the left bank of the Sava River.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69ca828153f48190bdb27ac46f8e0745 completed March 30, 2026, 2:02 p.m.
NER Named-entity recognition batch_69caf97247c481908b18287eb7ee0a53 completed March 30, 2026, 10:30 p.m.
NED1 Entity disambiguation (via context triple) batch_69cb149266e88190a582b11d68702a6a completed March 31, 2026, 12:25 a.m.
Created at: March 30, 2026, 4:40 p.m.