Triple

T12636044
Position Surface form Disambiguated ID Type / Status
Subject River Dahme E301765 entity
Predicate mouthRiver P4359 FINISHED
Object River Spree E226997 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: River Spree | Statement: [River Dahme, mouthRiver, River Spree]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: River Spree
Context triple: [River Dahme, mouthRiver, River Spree]
  • A. River Spree chosen
    River Spree is a major river flowing through Berlin, Germany, known for shaping the city’s landscape and passing many historic and cultural landmarks.
  • B. Oder-Spree
    Oder-Spree is a rural district in the eastern German state of Brandenburg, known for its lakes, forests, and towns along the Oder and Spree rivers.
  • C. Dahme
    Dahme is a small coastal town on the Baltic Sea in northern Germany, known for its beaches and seaside tourism.
  • D. Dahme
    The Dahme is a river in eastern Germany that flows through Brandenburg and Berlin before joining the Spree.
  • E. Unstrut River
    The Unstrut River is a tributary of the Saale in central Germany, flowing through Thuringia and Saxony-Anhalt and known for its scenic valleys, vineyards, and historic towns.
  • 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_69d7bdec9f9c8190b4bac675b7588211 completed April 9, 2026, 2:55 p.m.
NER Named-entity recognition batch_69d96147f49c8190b701e1e27e207a95 completed April 10, 2026, 8:44 p.m.
NED1 Entity disambiguation (via context triple) batch_69f7c0d12cd88190b6e205ad98296ed9 completed May 3, 2026, 9:40 p.m.
Created at: April 9, 2026, 5:16 p.m.