Triple

T2164408
Position Surface form Disambiguated ID Type / Status
Subject Emil Adolf von Behring E46874 entity
Predicate workLocation P7 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: [Emil Adolf von Behring, workLocation, Marburg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Marburg
Context triple: [Emil Adolf von Behring, workLocation, 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. Neustadt
    Neustadt is a district of the Austrian city of Salzburg, known for its central urban character within the historic and cultural landscape of the city.
  • C. Dillenburg
    Dillenburg is a historic town in the German state of Hesse, known as the ancestral seat of the House of Orange-Nassau and its connection to Dutch history.
  • D. Soest
    Soest is a historic town in North Rhine-Westphalia, Germany, known for its well-preserved medieval architecture and former significance as a Hanseatic trading center.
  • E. Heidelberg
    Heidelberg is a historic university city in southwestern Germany renowned for its picturesque old town, castle ruins, and one of Europe’s oldest universities.
  • 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_69a88a184cbc8190877791f6552c2484 completed March 4, 2026, 7:38 p.m.
NER Named-entity recognition batch_69abbe8e1cfc81908adc0357ddfec701 completed March 7, 2026, 5:58 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae5d98286c8190a6890e2c7daa10b5 completed March 9, 2026, 5:41 a.m.
Created at: March 4, 2026, 7:45 p.m.