Triple

T6845705
Position Surface form Disambiguated ID Type / Status
Subject Villeneuve d’Ascq E157888 entity
Predicate hasTwinTown P919 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: [Villeneuve d’Ascq, hasTwinTown, Tübingen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tübingen
Context triple: [Villeneuve d’Ascq, hasTwinTown, 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_69c6882ed4c081909dc465a7cf8838be completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6d7cbe4488190b41ddf953f12f55f completed March 27, 2026, 7:17 p.m.
NED1 Entity disambiguation (via context triple) batch_69ca47023570819082904801962f8dcb completed March 30, 2026, 9:48 a.m.
Created at: March 27, 2026, 2:19 p.m.