Triple

T4525002
Position Surface form Disambiguated ID Type / Status
Subject Queen Silvia of Sweden E103355 entity
Predicate placeOfBirth P1 FINISHED
Object Heidelberg, Germany E15415 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: Heidelberg, Germany | Statement: [Queen Silvia of Sweden, placeOfBirth, Heidelberg, Germany]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Heidelberg, Germany
Context triple: [Queen Silvia of Sweden, placeOfBirth, Heidelberg, Germany]
  • A. Tübingen, Germany
    Tübingen, Germany, is a historic university town in the state of Baden-Württemberg known for its medieval old town and renowned Eberhard Karls University.
  • B. Heidelberg chosen
    Heidelberg is a historic university city in southwestern Germany renowned for its picturesque old town, castle ruins, and one of Europe’s oldest universities.
  • C. Heidelberg
    Heidelberg is a suburb of Melbourne, Australia, known for its historic role in Australian Impressionism and its location along the Yarra River.
  • D. Giessen, Germany
    Giessen, Germany is a central German university town in the state of Hesse, known for its large student population and academic institutions.
  • E. Weinheim, Germany
    Weinheim, Germany is a town in the state of Baden-Württemberg known for its historic old town, twin castles, and role as a regional economic and publishing center.
  • 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_69bd43dba59881908cf59b31df8c7ae1 completed March 20, 2026, 12:55 p.m.
NER Named-entity recognition batch_69bd577490f48190ac1fb3cbf3d8a41e completed March 20, 2026, 2:19 p.m.
NED1 Entity disambiguation (via context triple) batch_69bda4483aa481909a100bf20085d667 completed March 20, 2026, 7:47 p.m.
Created at: March 20, 2026, 1:03 p.m.