Triple

T5062316
Position Surface form Disambiguated ID Type / Status
Subject Bourges E114049 entity
Predicate twinTown P1072 FINISHED
Object Tübingen E306426 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: Tübingen | Statement: [Bourges, twinTown, Tübingen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tübingen
Context triple: [Bourges, twinTown, Tübingen]
  • A. Tübingen chosen
    Tübingen is a historic university town in southwestern Germany known for its well-preserved medieval old town and prestigious Eberhard Karls University.
  • 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. 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.
  • E. Heilbronn
    Heilbronn is a city in the German state of Baden-Württemberg known for its industrial base, wine production, and role as a regional economic and educational hub.
  • 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_69bd443c0c8c81908663b77afb28e165 completed March 20, 2026, 12:57 p.m.
NER Named-entity recognition batch_69bd7475be3c819085cde8ec544c407e completed March 20, 2026, 4:23 p.m.
NED1 Entity disambiguation (via context triple) batch_69c712687f408190ac948dcc990e8e08 completed March 27, 2026, 11:27 p.m.
Created at: March 20, 2026, 1:38 p.m.