Triple

T2962161
Position Surface form Disambiguated ID Type / Status
Subject Brussels–Namur railway line E80073 entity
Predicate terminus P388 FINISHED
Object Namur E107798 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: Namur | Statement: [Brussels–Namur railway line, terminus, Namur]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Namur
Context triple: [Brussels–Namur railway line, terminus, Namur]
  • A. Namur chosen
    Namur is a historic Belgian city and the capital of Wallonia, located at the confluence of the Meuse and Sambre rivers.
  • B. Nivelles
    Nivelles is a historic town in present-day Belgium known for its medieval architecture, including the Romanesque Collegiate Church of Saint Gertrude.
  • C. Binche
    Binche is a historic town in the Walloon region of Belgium, renowned for its well-preserved medieval architecture and its UNESCO-recognized Carnival of Binche.
  • D. Liège
    Liège is a major city in eastern Belgium known for its industrial heritage, vibrant cultural scene, and position along the Meuse River.
  • E. Eupen
    Eupen is a town in eastern Belgium that serves as the administrative center of the country’s German-speaking Community.
  • 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_69ad8b1341848190bd19dbf46892887d completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69ad9955e6488190bea170724d5fbfe8 completed March 8, 2026, 3:44 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5d02c25408190ba366a6a5e422046 completed March 14, 2026, 9:16 p.m.
Created at: March 8, 2026, 2:57 p.m.