Triple

T1163983
Position Surface form Disambiguated ID Type / Status
Subject Louis Marie Cordonnier E24557 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: [Louis Marie Cordonnier, workLocation, Lille]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lille
Context triple: [Louis Marie Cordonnier, 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. 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.
  • C. Lyon
    Lyon is a major city in east-central France known for its historical and architectural landmarks, gastronomy, and role as a key economic and cultural center.
  • D. Strasbourg
    Strasbourg is a major French city on the Rhine known for hosting key European institutions, including the European Parliament and the Council of Europe.
  • E. Nantes
    Nantes is a historic port city in western France on the Loire River, known for its maritime heritage, cultural institutions, and vibrant arts scene.
  • 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_69a494060e148190abb42f971242c197 completed March 1, 2026, 7:31 p.m.
NER Named-entity recognition batch_69a4bcc9dc5081908e225a485186ab12 completed March 1, 2026, 10:25 p.m.
NED1 Entity disambiguation (via context triple) batch_69aca2e36fc081908de3b67293c7bbf6 completed March 7, 2026, 10:12 p.m.
Created at: March 1, 2026, 7:45 p.m.