Triple

T10381593
Position Surface form Disambiguated ID Type / Status
Subject Pierre De Geyter E244653 entity
Predicate residence P75 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: [Pierre De Geyter, residence, Lille]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lille
Context triple: [Pierre De Geyter, residence, 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. Métropole Européenne de Lille
    Métropole Européenne de Lille is a major French intercommunal metropolitan authority centered on the city of Lille, coordinating urban planning, transport, and development across numerous surrounding municipalities in northern France.
  • C. 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.
  • D. Lillebonne
    Lillebonne is a historic town in northern France’s Normandy region, known for its Roman archaeological remains and medieval heritage.
  • E. 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.
  • 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_69d381b3e328819094b23b8edcd29b5a completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d4e9921fa48190a874aa9a9e385b97 completed April 7, 2026, 11:25 a.m.
NED1 Entity disambiguation (via context triple) batch_69d79592512c8190b999191f16e3133c completed April 9, 2026, 12:03 p.m.
Created at: April 6, 2026, 12:03 p.m.