Triple

T4704765
Position Surface form Disambiguated ID Type / Status
Subject Váh E104365 entity
Predicate flowsThrough P225 FINISHED
Object Trenčín Region E346735 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: Trenčín Region | Statement: [Váh, flowsThrough, Trenčín Region]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Trenčín Region
Context triple: [Váh, flowsThrough, Trenčín Region]
  • A. Trenčín Region chosen
    Trenčín Region is an administrative region in western Slovakia known for its historic towns, including the city of Trenčín, and its cultural and economic significance.
  • B. Žilina Region
    Žilina Region is an administrative region in northern Slovakia known for its mountainous landscapes, cultural heritage, and important industrial and transport centers.
  • C. Trnava Region
    Trnava Region is an administrative region in western Slovakia known for its historic towns, agricultural landscape, and proximity to the capital, Bratislava.
  • D. Prešov Region
    The Prešov Region is an administrative region in northeastern Slovakia known for its mountainous landscapes, historic towns, and proximity to the High Tatras.
  • E. Košice Region
    Košice Region is an administrative region in eastern Slovakia that includes the city of Košice as its largest urban center.
  • 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_69bd43e9b88481908582103dcadff3d9 completed March 20, 2026, 12:56 p.m.
NER Named-entity recognition batch_69bd63d1e9c48190bad5f7d68bf0f622 completed March 20, 2026, 3:12 p.m.
NED1 Entity disambiguation (via context triple) batch_69be81a32f18819093c08d05039442c4 completed March 21, 2026, 11:31 a.m.
Created at: March 20, 2026, 1:17 p.m.