Triple

T9415049
Position Surface form Disambiguated ID Type / Status
Subject River Spree E226997 entity
Predicate flowsThrough P225 FINISHED
Object Cottbus E203175 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: Cottbus | Statement: [River Spree, flowsThrough, Cottbus]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Cottbus
Context triple: [River Spree, flowsThrough, Cottbus]
  • A. Cottbus chosen
    Cottbus is a city in eastern Germany known as a regional center for science and technology, including aerospace research.
  • B. Magdeburg
    Magdeburg is a historic city in central Germany, known for its medieval cathedral, role as a major trading and industrial center, and location on the Elbe River.
  • C. Chemnitz
    Chemnitz is a city in eastern Germany known for its industrial heritage and post-reunification urban redevelopment.
  • D. Leipzig
    Leipzig is a major city in eastern Germany known for its rich cultural heritage, vibrant music and arts scene, and important role in trade and commerce.
  • E. Neustrelitz
    Neustrelitz is a town in northeastern Germany known for hosting a key research center of the German Aerospace Center (DLR), particularly focused on satellite data and space-related technologies.
  • 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_69ca84359e7c819091148ba4b670e436 completed March 30, 2026, 2:09 p.m.
NER Named-entity recognition batch_69cd68c7bd648190b17f082883c98239 completed April 1, 2026, 6:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69d22840c4548190b1610e2c3cec6220 completed April 5, 2026, 9:15 a.m.
Created at: March 30, 2026, 7:48 p.m.