Triple

T3332036
Position Surface form Disambiguated ID Type / Status
Subject Ramin Golestanian E70053 entity
Predicate workLocation P7 FINISHED
Object Göttingen E25610 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: Göttingen | Statement: [Ramin Golestanian, workLocation, Göttingen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Göttingen
Context triple: [Ramin Golestanian, workLocation, Göttingen]
  • A. Göttingen chosen
    Göttingen is a historic university city in Lower Saxony, Germany, renowned for its prestigious Georg-August University and contributions to science and mathematics.
  • B. Darmstadt
    Darmstadt is a city in the German state of Hesse known for its historical ties to the Grand Duchy of Hesse and its role as a center of science, technology, and Art Nouveau culture.
  • C. Greifswald
    Greifswald is a historic Hanseatic university city in northeastern Germany, located near the Baltic Sea.
  • D. Heidelberg
    Heidelberg is a suburb of Melbourne, Australia, known for its historic role in Australian Impressionism and its location along the Yarra River.
  • 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_69ad85a24f208190bcf83131bfed3521 completed March 8, 2026, 2:20 p.m.
NER Named-entity recognition batch_69adb19358e48190a503af01b92273a4 completed March 8, 2026, 5:27 p.m.
NED1 Entity disambiguation (via context triple) batch_69b4c3667a288190aa79835f5d621942 completed March 14, 2026, 2:09 a.m.
Created at: March 8, 2026, 3:12 p.m.