Triple

T9263081
Position Surface form Disambiguated ID Type / Status
Subject German Fairy Tale Route E222625 entity
Predicate passesThrough P225 FINISHED
Object Marburg E174796 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: Marburg | Statement: [German Fairy Tale Route, passesThrough, Marburg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Marburg
Context triple: [German Fairy Tale Route, passesThrough, Marburg]
  • A. Marburg chosen
    Marburg is a historic university town in central Germany known for its well-preserved medieval old town and the Philipps-Universität, one of the oldest Protestant universities in the world.
  • B. Vienenburg
    Vienenburg is a district of Goslar in Lower Saxony, Germany, known for its historic town center and proximity to the Harz Mountains.
  • C. Riemst
    Riemst is a municipality in the Belgian province of Limburg, known for its rural character and location near the borders with the Netherlands and Germany.
  • D. Meerbusch
    Meerbusch is a town in the German state of North Rhine-Westphalia, situated on the west bank of the Rhine near Düsseldorf and known for its affluent residential areas and green surroundings.
  • E. Landsberg
    Landsberg is a town in the Saalekreis district of the German state of Saxony-Anhalt.
  • 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_69ca841f2e808190a64f4c31903a1332 completed March 30, 2026, 2:09 p.m.
NER Named-entity recognition batch_69cd0719eee081909e21ecb9d6dd0b49 completed April 1, 2026, 11:52 a.m.
NED1 Entity disambiguation (via context triple) batch_69d1077d8a048190b4b45419fb0d7279 completed April 4, 2026, 12:43 p.m.
Created at: March 30, 2026, 7:32 p.m.