Triple

T9274616
Position Surface form Disambiguated ID Type / Status
Subject Southern Estonia E222913 entity
Predicate hasCity P316 FINISHED
Object Jõgeva
Jõgeva is a small town in eastern Estonia known as a local administrative and cultural center and for recording some of the country’s lowest winter temperatures.
 E798790 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: Jõgeva | Statement: [Southern Estonia, hasCity, Jõgeva]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Jõgeva
Context triple: [Southern Estonia, hasCity, Jõgeva]
  • A. Viljandi
    Viljandi is a historic town in southern Estonia known for its medieval castle ruins, rich cultural life, and annual folk music festival.
  • B. Põlva
    Põlva is a small town in southeastern Estonia known as a local administrative and cultural center surrounded by lakes and forested landscapes.
  • C. Tartu
    Tartu is Estonia’s second-largest city and a historic cultural and intellectual center, best known as the country’s main university town.
  • D. Viedma
    Viedma is a city in northern Patagonia and one of the oldest settlements in Argentina, serving as the capital of Río Negro Province.
  • E. Pärnu
    Pärnu is a coastal city in southwestern Estonia known as a popular summer resort and spa destination on the Baltic Sea.
  • 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: Jõgeva
Triple: [Southern Estonia, hasCity, Jõgeva]
Generated description
Jõgeva is a small town in eastern Estonia known as a local administrative and cultural center and for recording some of the country’s lowest winter temperatures.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Jõgeva
Target entity description: Jõgeva is a small town in eastern Estonia known as a local administrative and cultural center and for recording some of the country’s lowest winter temperatures.
  • A. Viljandi
    Viljandi is a historic town in southern Estonia known for its medieval castle ruins, rich cultural life, and annual folk music festival.
  • B. Põlva
    Põlva is a small town in southeastern Estonia known as a local administrative and cultural center surrounded by lakes and forested landscapes.
  • C. Tartu
    Tartu is Estonia’s second-largest city and a historic cultural and intellectual center, best known as the country’s main university town.
  • D. Viedma
    Viedma is a city in northern Patagonia and one of the oldest settlements in Argentina, serving as the capital of Río Negro Province.
  • E. Pärnu
    Pärnu is a coastal city in southwestern Estonia known as a popular summer resort and spa destination on the Baltic Sea.
  • 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_69ca841ffe208190aa7bcffbef2f8379 completed March 30, 2026, 2:09 p.m.
NER Named-entity recognition batch_69cd078a045c8190b2c4d1ec64b932ad completed April 1, 2026, 11:54 a.m.
NED1 Entity disambiguation (via context triple) batch_69d1101359048190b37547d1fedb3bb1 completed April 4, 2026, 1:20 p.m.
NEDg Description generation batch_69d110ca72088190b9a65529801784a5 completed April 4, 2026, 1:23 p.m.
NED2 Entity disambiguation (via description) batch_69d1112224488190aeae685a1681539f completed April 4, 2026, 1:24 p.m.
Created at: March 30, 2026, 7:33 p.m.