Triple

T1494408
Position Surface form Disambiguated ID Type / Status
Subject Louis Pasteur E29652 entity
Predicate workLocation P7 FINISHED
Object Lille, France 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, France | Statement: [Louis Pasteur, workLocation, Lille, France]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lille, France
Context triple: [Louis Pasteur, workLocation, Lille, France]
  • A. 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.
  • B. 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.
  • C. European Metropolis of Lille
    The European Metropolis of Lille is an intercommunal metropolitan authority in northern France that groups Lille and surrounding communes into a single administrative and economic urban area.
  • D. Amiens, France
    Amiens, France is a historic city in northern France known for its Gothic cathedral and as the birthplace of French President Emmanuel Macron.
  • 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_69a498dba1d8819093b46a3a8d2485f1 completed March 1, 2026, 7:51 p.m.
NER Named-entity recognition batch_69a4c6c78c9481909b210b845aa6e9df completed March 1, 2026, 11:07 p.m.
NED1 Entity disambiguation (via context triple) batch_69ad1cabe25c8190ba1d285a210a00f0 completed March 8, 2026, 6:52 a.m.
Created at: March 1, 2026, 8:12 p.m.