Triple

T7966039
Position Surface form Disambiguated ID Type / Status
Subject Colditz E185207 entity
Predicate district P2709 FINISHED
Object Leipzig district E35114 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: Leipzig district | Statement: [Colditz, district, Leipzig district]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Leipzig district
Context triple: [Colditz, district, Leipzig district]
  • A. District of Leipzig chosen
    The District of Leipzig is a rural administrative district in the Free State of Saxony in eastern Germany, surrounding but not including the city of Leipzig and forming part of the Central German Metropolitan Region.
  • B. Bezirk Leipzig
    Bezirk Leipzig was an administrative district in the former East Germany centered around the city of Leipzig.
  • C. 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.
  • D. Leipzig metropolitan region
    The Leipzig metropolitan region is a major urban and economic area in eastern Germany centered on the city of Leipzig and its surrounding cities and towns.
  • E. Babelsberg district
    Babelsberg district is a historic quarter of Potsdam, Germany, known for its film studios, lakeside villas, and well-preserved 19th- and early 20th-century architecture.
  • 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_69ca8297699481909b75a405f01e03af completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cb3ba262208190887169fe94e47b0e completed March 31, 2026, 3:12 a.m.
NED1 Entity disambiguation (via context triple) batch_69cc9387aabc81909ca13ee6a51525f4 completed April 1, 2026, 3:39 a.m.
Created at: March 30, 2026, 5:12 p.m.