Triple

T4802318
Position Surface form Disambiguated ID Type / Status
Subject Carmen de Patagones E106862 entity
Predicate hasNearbyCity P350 FINISHED
Object Viedma E106861 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: Viedma | Statement: [Carmen de Patagones, hasNearbyCity, Viedma]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Viedma
Context triple: [Carmen de Patagones, hasNearbyCity, Viedma]
  • A. Viedma chosen
    Viedma is a city in northern Patagonia and one of the oldest settlements in Argentina, serving as the capital of Río Negro Province.
  • B. Viljandi
    Viljandi is a historic town in southern Estonia known for its medieval castle ruins, rich cultural life, and annual folk music festival.
  • C. Pärnu
    Pärnu is a coastal city in southwestern Estonia known as a popular summer resort and spa destination on the Baltic Sea.
  • D. Jõepere
    Jõepere is a village in Estonia known as the birthplace of the national writer Friedrich Reinhold Kreutzwald.
  • E. Kuressaare
    Kuressaare is the main town on Estonia’s Saaremaa island, known for its well-preserved medieval castle and seaside spa resort atmosphere.
  • 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_69bd43f6a1e08190bf0a372bfc336ee5 completed March 20, 2026, 12:56 p.m.
NER Named-entity recognition batch_69bd6c42113081908824f3608e65cbff completed March 20, 2026, 3:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69be4d9e1ea88190b098d5203bd1d145 completed March 21, 2026, 7:49 a.m.
Created at: March 20, 2026, 1:23 p.m.