Triple

T10217188
Position Surface form Disambiguated ID Type / Status
Subject Picardy E242475 entity
Predicate contains P35 FINISHED
Object Compiègne E173943 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: Compiègne | Statement: [Picardy, contains, Compiègne]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Compiègne
Context triple: [Picardy, contains, Compiègne]
  • A. Compiegne chosen
    Compiègne is a historic city in northern France known for its royal château, forest, and role in significant events such as the signing of the 1918 Armistice.
  • B. Creil
    Creil is a commuter town in northern France’s Oise department, known as a regional rail hub connecting Paris with Picardy via major train and RER lines.
  • C. Reims
    Reims is a historic city in northeastern France known for its Gothic cathedral, role in French coronations, and significance during both World Wars.
  • D. Soissons
    Soissons is a historic town in northern France known for its strategic military importance and notable battles throughout European history.
  • E. Tournus
    Tournus is a historic town in eastern France’s Burgundy region, known for its Romanesque abbey and riverside setting along the Saône.
  • 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_69d381ae26c48190985abd0e25ee5d04 completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d3aa6e544c8190961cdd7f1fbe24e6 completed April 6, 2026, 12:43 p.m.
NED1 Entity disambiguation (via context triple) batch_69d71c77f65c8190862fde1c2fae045b completed April 9, 2026, 3:26 a.m.
Created at: April 6, 2026, 11:06 a.m.