Triple

T9481423
Position Surface form Disambiguated ID Type / Status
Subject Maubeuge E228647 entity
Predicate nearbyCity P350 FINISHED
Object Charleroi E95883 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: Charleroi | Statement: [Maubeuge, nearbyCity, Charleroi]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Charleroi
Context triple: [Maubeuge, nearbyCity, Charleroi]
  • A. Charleroi chosen
    Charleroi is a major industrial city in the Walloon region of Belgium, historically important as a fortified stronghold and later as a center of coal mining and heavy industry.
  • B. Tervuren
    Tervuren is a municipality in Flemish Brabant, Belgium, known for its historic park, royal connections, and the Royal Museum for Central Africa.
  • C. Mechelen
    Mechelen is a historic city in the Flemish region of Belgium, known for its rich architectural heritage, medieval center, and prominent role in the Low Countries’ political and religious history.
  • 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. Gosselies
    Gosselies is a district of the city of Charleroi in Wallonia, Belgium, known for its proximity to Brussels South Charleroi Airport and its industrial and aeronautical activities.
  • 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_69ca84730a5081908de282651019bf2f completed March 30, 2026, 2:10 p.m.
NER Named-entity recognition batch_69cd8018645c8190823d82a93635b345 completed April 1, 2026, 8:29 p.m.
NED1 Entity disambiguation (via context triple) batch_69d29999e9248190b2f1900fa7ad3da5 completed April 5, 2026, 5:19 p.m.
Created at: March 30, 2026, 7:54 p.m.