Triple

T11591179
Position Surface form Disambiguated ID Type / Status
Subject IPBES Secretariat E274883 entity
Predicate headquartersLocation P62 FINISHED
Object Bonn, Germany E23133 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: Bonn, Germany | Statement: [IPBES Secretariat, headquartersLocation, Bonn, Germany]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bonn, Germany
Context triple: [IPBES Secretariat, headquartersLocation, Bonn, Germany]
  • A. Bonn chosen
    Bonn is a historic German city on the Rhine River, best known for being the birthplace of Ludwig van Beethoven and the former seat of the federal government before reunification.
  • B. Frankfort, Germany
    Frankfort, Germany is a German city whose name has been used for places abroad, including the village of Frankfort in Illinois, USA.
  • C. Johnsburg, Germany
    Johnsburg, Germany is a German town that served as the namesake and ancestral origin for many of the settlers of Johnsburg, Illinois.
  • D. Linden, Germany
    Linden, Germany is a small town in the state of Hesse known for its historical roots and traditional German character.
  • E. Bergen, Germany
    Bergen, Germany is a small town in Lower Saxony best known for its proximity to the former Bergen-Belsen concentration camp and its historical military significance.
  • 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_69d6aae6b14c81908dc5a74bad7591f9 completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d894643ae48190837502b713f5b9c6 completed April 10, 2026, 6:10 a.m.
NED1 Entity disambiguation (via context triple) batch_69e714634b308190bdcb761f8b6712e7 completed April 21, 2026, 6:08 a.m.
Created at: April 8, 2026, 9:38 p.m.