Triple

T9893687
Position Surface form Disambiguated ID Type / Status
Subject Spremberg E181515 entity
Predicate locatedInFormerDistrict P20255 FINISHED
Object Bezirk Cottbus E543213 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: Bezirk Cottbus | Statement: [Spremberg, locatedInFormerDistrict, Bezirk Cottbus]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bezirk Cottbus
Context triple: [Spremberg, locatedInFormerDistrict, Bezirk Cottbus]
  • A. Bezirk Cottbus chosen
    Bezirk Cottbus was an administrative district in the former East Germany, located in the southeast of the country and centered around the city of Cottbus.
  • B. Bezirk Dresden
    Bezirk Dresden was an administrative district centered on the city of Dresden that functioned as one of the key regional divisions of the former East Germany.
  • C. Bezirk Gera
    Bezirk Gera was an administrative district of the former East Germany, centered around the city of Gera in the state of Thuringia.
  • D. Kreis Sebnitz
    Kreis Sebnitz was a former rural district in the German Democratic Republic, located in the Bezirk Dresden region of Saxony and centered around the town of Sebnitz.
  • E. Bezirk Leipzig
    Bezirk Leipzig was an administrative district in the former East Germany centered around the city of Leipzig.
  • 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_69ca8283a6708190801af7a25a7ebb9f completed March 30, 2026, 2:02 p.m.
NER Named-entity recognition batch_69cdb48271d48190b718c7f6b2fe315b completed April 2, 2026, 12:12 a.m.
NED1 Entity disambiguation (via context triple) batch_69d1eb0d984c81908408f90f156624e5 completed April 5, 2026, 4:54 a.m.
Created at: March 30, 2026, 8:39 p.m.