Triple

T3688024
Position Surface form Disambiguated ID Type / Status
Subject Mulde E78272 entity
Predicate flowsThrough P225 FINISHED
Object Saxony E11465 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: Saxony | Statement: [Mulde, flowsThrough, Saxony]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Saxony
Context triple: [Mulde, flowsThrough, Saxony]
  • A. Saxony chosen
    Saxony is a historic region and former kingdom in eastern Germany, known for its cultural centers like Dresden and Leipzig and its significant role in Central European history.
  • B. Thuringia
    Thuringia is a federal state in central Germany known for its forested landscapes, historic cities like Weimar and Erfurt, and its rich cultural and intellectual heritage.
  • C. Brandenburg
    Brandenburg is a federal state in northeastern Germany that surrounds Berlin and is known for its lakes, forests, and historic Prussian heritage.
  • D. Bavaria
    Bavaria is a historic region and federal state in southeastern Germany, known for its distinct cultural traditions, large size and population, and major cities such as Munich.
  • E. Saxony-Anhalt
    Saxony-Anhalt is a federal state in central Germany known for its rich cultural heritage, including numerous UNESCO World Heritage Sites such as the Bauhaus in Dessau and the historic towns of Quedlinburg and Wittenberg.
  • 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_69ad85e285a081908f8cbfa9e2ed9b75 completed March 8, 2026, 2:21 p.m.
NER Named-entity recognition batch_69adc4c960788190b73ede08658846aa completed March 8, 2026, 6:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69bdacb883188190a213986b2816cde2 completed March 20, 2026, 8:23 p.m.
Created at: March 8, 2026, 3:26 p.m.