Triple

T1890404
Position Surface form Disambiguated ID Type / Status
Subject Starnberger See E41860 entity
Predicate hasShoreSettlement P16159 FINISHED
Object Starnberg E42787 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: Starnberg | Statement: [Starnberger See, hasShoreSettlement, Starnberg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Starnberg
Context triple: [Starnberger See, hasShoreSettlement, Starnberg]
  • A. Starnberg chosen
    Starnberg is a lakeside town in Bavaria, Germany, known for its affluent residential character and scenic location on Lake Starnberg southwest of Munich.
  • B. Landsberg
    Landsberg is a town in the Saalekreis district of the German state of Saxony-Anhalt.
  • C. Idstein
    Idstein is a historic town in the German state of Hesse, known for its well-preserved medieval old town and timber-framed architecture.
  • D. Brackenheim
    Brackenheim is a small town in the German state of Baden-Württemberg, best known as the birthplace of Theodor Heuss, the first President of the Federal Republic of Germany.
  • E. Landsberg am Lech
    Landsberg am Lech is a historic Bavarian town in southern Germany known for its medieval old town, picturesque setting on the Lech River, and its association with the nearby Landsberg Prison.
  • 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_69a8864b6de0819098d089f6a1b910a7 completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69abb14475448190b291ada3454bf98b completed March 7, 2026, 5:01 a.m.
NED1 Entity disambiguation (via context triple) batch_69af2b4b974081908da05bc63f923215 completed March 9, 2026, 8:19 p.m.
Created at: March 4, 2026, 7:34 p.m.