Triple

T909728
Position Surface form Disambiguated ID Type / Status
Subject Montenegro E19630 entity
Predicate hasCity P316 FINISHED
Object Nikšić
Nikšić is one of the largest cities in Montenegro, known as an important industrial, cultural, and educational center of the country.
 E141456 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: Nikšić | Statement: [Montenegro, hasCity, Nikšić]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Nikšić
Context triple: [Montenegro, hasCity, Nikšić]
  • A. 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.
  • B. Zrenjanin
    Zrenjanin is a city in northern Serbia known as an economic, cultural, and administrative center of the Banat region.
  • C. Monastir
    Monastir, known today as Bitola in North Macedonia, is a historic Balkan city that played a significant strategic role during World War I.
  • D. Šabac
    Šabac is a historic city in western Serbia on the Sava River, known as a regional cultural and educational center.
  • E. Podgorica
    Podgorica is the capital and largest city of Montenegro, serving as its political, economic, and cultural center in the Balkans.
  • 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: Nikšić
Triple: [Montenegro, hasCity, Nikšić]
Generated description
Nikšić is one of the largest cities in Montenegro, known as an important industrial, cultural, and educational center of the country.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Nikšić
Target entity description: Nikšić is one of the largest cities in Montenegro, known as an important industrial, cultural, and educational center of the country.
  • A. 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.
  • B. Zrenjanin
    Zrenjanin is a city in northern Serbia known as an economic, cultural, and administrative center of the Banat region.
  • C. Monastir
    Monastir, known today as Bitola in North Macedonia, is a historic Balkan city that played a significant strategic role during World War I.
  • D. Šabac
    Šabac is a historic city in western Serbia on the Sava River, known as a regional cultural and educational center.
  • E. Podgorica
    Podgorica is the capital and largest city of Montenegro, serving as its political, economic, and cultural center in the Balkans.
  • 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_69a4939f91a08190ba68c2c81eab90fe completed March 1, 2026, 7:29 p.m.
NER Named-entity recognition batch_69a4b2dca5208190bc9f17cd9dd6a98f completed March 1, 2026, 9:42 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac89f641c48190b7cff1073852a228 completed March 7, 2026, 8:26 p.m.
NEDg Description generation batch_69ac8a6805c88190a54d219e2b46afb3 completed March 7, 2026, 8:28 p.m.
NED2 Entity disambiguation (via description) batch_69ac8ad15e7c8190b596ee5f4b9d0469 completed March 7, 2026, 8:30 p.m.
Created at: March 1, 2026, 7:39 p.m.