Triple

T9538708
Position Surface form Disambiguated ID Type / Status
Subject Morges E230089 entity
Predicate hasTwinTown P919 FINISHED
Object Prijedor E327040 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: Prijedor | Statement: [Morges, hasTwinTown, Prijedor]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Prijedor
Context triple: [Morges, hasTwinTown, Prijedor]
  • A. Prijedor chosen
    Prijedor is a city in northwestern Bosnia and Herzegovina known for its industrial heritage and its significant role and tragic events during the Bosnian War.
  • B. Prokuplje
    Prokuplje is a town and municipality in southern Serbia known for its historical heritage and agricultural surroundings.
  • C. Đakovo
    Đakovo is a historic town in eastern Croatia renowned for its cathedral, cultural heritage, and role as a regional center in Slavonia.
  • D. Prijepolje
    Prijepolje is a town in southwestern Serbia near the border with Montenegro, known for its multicultural heritage and scenic location along the Lim River.
  • E. Zrenjanin
    Zrenjanin is a city in northern Serbia known as an economic, cultural, and administrative center of the Banat region.
  • 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_69ca847b1b3081908f72bc932c17cc41 completed March 30, 2026, 2:11 p.m.
NER Named-entity recognition batch_69cd98cfead8819089a8f47ea83500a4 completed April 1, 2026, 10:14 p.m.
NED1 Entity disambiguation (via context triple) batch_69d14c5684308190a9cb2942f7d2f2b0 completed April 4, 2026, 5:37 p.m.
Created at: March 30, 2026, 8:01 p.m.