Triple

T2135106
Position Surface form Disambiguated ID Type / Status
Subject Jan van Eyck E46631 entity
Predicate workLocation P7 FINISHED
Object Lille E18284 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: Lille | Statement: [Jan van Eyck, workLocation, Lille]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lille
Context triple: [Jan van Eyck, workLocation, Lille]
  • A. Lille chosen
    Lille is a historic industrial and cultural hub in northern France, known for its Flemish-influenced architecture, large student population, and role as a major European transport crossroads.
  • B. Lille Europe
    Lille Europe is a major high-speed railway station in Lille, France, serving international Eurostar and TGV services between the UK and continental Europe.
  • C. Valenciennes
    Valenciennes is a historic industrial city in northern France near the Belgian border, known for its former coal and steel industries and its rich artistic and architectural heritage.
  • D. Amiens
    Amiens is a historic city in northern France, known for its Gothic cathedral and role as the site of the 1802 Treaty of Amiens.
  • E. Saint-Omer
    Saint-Omer is a historic town in northern France known for its medieval architecture, strategic military importance, and role in Franco-Spanish conflicts.
  • 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_69a88a174ab48190a5db20c132e5dccf completed March 4, 2026, 7:37 p.m.
NER Named-entity recognition batch_69abbdc3a12081908e95ae870207367f completed March 7, 2026, 5:55 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae6aee10b08190abeb6059d4d2ad0a completed March 9, 2026, 6:38 a.m.
Created at: March 4, 2026, 7:44 p.m.