Triple

T3879644
Position Surface form Disambiguated ID Type / Status
Subject Bačka E92788 entity
Predicate borders P224 FINISHED
Object Danube E12683 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: Danube | Statement: [Bačka, borders, Danube]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Danube
Context triple: [Bačka, borders, Danube]
  • A. Danube chosen
    The Danube is one of Europe's longest and most historically significant rivers, flowing from Germany to the Black Sea and passing through numerous Central and Eastern European countries.
  • B. Elbe
    The Elbe is one of Central Europe's major rivers, flowing from the Czech Republic through Germany to the North Sea and serving as an important waterway for transport, industry, and agriculture.
  • C. Drava
    The Drava is a major Central European river that flows through countries including Italy, Austria, Slovenia, Croatia, and Hungary before joining the Danube.
  • D. Traisen
    Traisen is a river in northeastern Austria that flows through Lower Austria before joining the Danube.
  • E. Tisza
    The Tisza is one of Central Europe's significant rivers, flowing through several countries including Hungary before joining the Danube.
  • 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_69aed9697de0819087c2559295ff3d12 completed March 9, 2026, 2:30 p.m.
NER Named-entity recognition batch_69aeec7438808190b6c90fcb3000ebe9 completed March 9, 2026, 3:51 p.m.
NED1 Entity disambiguation (via context triple) batch_69b589bcbce88190b97b9dcec8a976f4 completed March 14, 2026, 4:15 p.m.
Created at: March 9, 2026, 3:20 p.m.