Triple

T1163708
Position Surface form Disambiguated ID Type / Status
Subject Belgrade E24551 entity
Predicate hasMunicipality P847 FINISHED
Object Čukarica
Čukarica is a municipality of Belgrade known for its mix of urban neighborhoods, industrial zones, and green areas along the Sava River.
 E149423 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: Čukarica | Statement: [Belgrade, hasMunicipality, Čukarica]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Čukarica
Context triple: [Belgrade, hasMunicipality, Čukarica]
  • A. Zrenjanin
    Zrenjanin is a city in northern Serbia known as an economic, cultural, and administrative center of the Banat region.
  • B. Nikšić
    Nikšić is one of the largest cities in Montenegro, known as an important industrial, cultural, and educational center of the country.
  • C. Zemun
    Zemun is a historic urban municipality of Belgrade, Serbia, known for its preserved old town, Danube riverfront, and distinctive Central European architectural heritage.
  • D. Niš
    Niš is one of the largest and oldest cities in Serbia, known as a key cultural, economic, and transportation hub in the southern part of the country.
  • E. Šabac
    Šabac is a historic city in western Serbia on the Sava River, known as a regional cultural and educational center.
  • 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: Čukarica
Triple: [Belgrade, hasMunicipality, Čukarica]
Generated description
Čukarica is a municipality of Belgrade known for its mix of urban neighborhoods, industrial zones, and green areas along the Sava River.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Čukarica
Target entity description: Čukarica is a municipality of Belgrade known for its mix of urban neighborhoods, industrial zones, and green areas along the Sava River.
  • A. Zrenjanin
    Zrenjanin is a city in northern Serbia known as an economic, cultural, and administrative center of the Banat region.
  • B. Nikšić
    Nikšić is one of the largest cities in Montenegro, known as an important industrial, cultural, and educational center of the country.
  • C. Zemun
    Zemun is a historic urban municipality of Belgrade, Serbia, known for its preserved old town, Danube riverfront, and distinctive Central European architectural heritage.
  • D. Niš
    Niš is one of the largest and oldest cities in Serbia, known as a key cultural, economic, and transportation hub in the southern part of the country.
  • E. Šabac
    Šabac is a historic city in western Serbia on the Sava River, known as a regional cultural and educational center.
  • 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_69a494060e148190abb42f971242c197 completed March 1, 2026, 7:31 p.m.
NER Named-entity recognition batch_69a4bcc9dc5081908e225a485186ab12 completed March 1, 2026, 10:25 p.m.
NED1 Entity disambiguation (via context triple) batch_69acb2f6f3e4819099310a5e21455c21 completed March 7, 2026, 11:21 p.m.
NEDg Description generation batch_69acb3ff59488190a05fd3400e76dd56 completed March 7, 2026, 11:25 p.m.
NED2 Entity disambiguation (via description) batch_69acb47f0b088190b3eb32101c5e6f89 completed March 7, 2026, 11:27 p.m.
Created at: March 1, 2026, 7:45 p.m.