Triple

T7320409
Position Surface form Disambiguated ID Type / Status
Subject Maastricht E168527 entity
Predicate hasNeighbouringMunicipality P224 FINISHED
Object Riemst E549181 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: Riemst | Statement: [Maastricht, hasNeighbouringMunicipality, Riemst]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Riemst
Context triple: [Maastricht, hasNeighbouringMunicipality, Riemst]
  • A. Riemst chosen
    Riemst is a municipality in the Belgian province of Limburg, known for its rural character and location near the borders with the Netherlands and Germany.
  • B. Rastatt
    Rastatt is a historic town in southwestern Germany, known for its Baroque architecture and its role as the site of significant early 18th-century peace negotiations.
  • C. Landsberg
    Landsberg is a town in the Saalekreis district of the German state of Saxony-Anhalt.
  • D. Rottweil
    Rottweil is a historic town in southwestern Germany known for its medieval architecture and as the namesake of the Rottweiler dog breed.
  • E. Neustadt
    Neustadt is a district of the Austrian city of Salzburg, known for its central urban character within the historic and cultural landscape of the city.
  • 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_69c68a5251508190ad68df4151cfeb04 completed March 27, 2026, 1:46 p.m.
NER Named-entity recognition batch_69c6ef1a7a3c81909504eb711056f302 completed March 27, 2026, 8:56 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7fa78d8e08190a5658f2df7fbf667 completed March 28, 2026, 3:57 p.m.
Created at: March 27, 2026, 3:02 p.m.