Triple

T11379516
Position Surface form Disambiguated ID Type / Status
Subject Frederick III the Pious E269556 entity
Predicate workLocation P7 FINISHED
Object Heidelberg 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 | Statement: [Frederick III the Pious, workLocation, Heidelberg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Heidelberg
Context triple: [Frederick III the Pious, workLocation, Heidelberg]
  • A. 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.
  • B. Heidelberg
    Heidelberg is a suburb of Melbourne, Australia, known for its historic role in Australian Impressionism and its location along the Yarra River.
  • C. Heidelberg
    Heidelberg is a South African town known for its historical significance and role as a regional service and commercial center.
  • D. Karlsruhe
    Karlsruhe is a major city in southwestern Germany best known as the seat of the country’s highest courts and a central hub of German constitutional jurisprudence.
  • E. Tübingen
    Tübingen is a historic university town in southwestern Germany known for its well-preserved medieval old town and prestigious Eberhard Karls University.
  • 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_69d6aacca1048190b39dbbc2174616fa completed April 8, 2026, 7:21 p.m.
NER Named-entity recognition batch_69d7fc30f5d48190bb273df4c9e583a9 completed April 9, 2026, 7:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69f64b7aff4c8190ba879e6c5632bb97 completed May 2, 2026, 7:07 p.m.
Created at: April 8, 2026, 9:34 p.m.