Triple

T11406489
Position Surface form Disambiguated ID Type / Status
Subject Middle Francia E270251 entity
Predicate includedRegion P285 FINISHED
Object Alsace E19573 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: Alsace | Statement: [Middle Francia, includedRegion, Alsace]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Alsace
Context triple: [Middle Francia, includedRegion, Alsace]
  • A. Alsace chosen
    Alsace is a historical and cultural region in northeastern France known for its blend of French and German influences, picturesque villages, and renowned wines.
  • B. Alsacia
    Alsacia is a Madrid Metro station on Line 2 serving the San Blas-Canillejas district in eastern Madrid, Spain.
  • C. Alsace-Lorraine
    Alsace-Lorraine is a historically contested border region between France and Germany, known for its mixed cultural heritage and strategic importance in European conflicts.
  • D. French Lorraine
    French Lorraine is a historical region in northeastern France whose culture reflects a blend of French and Germanic influences.
  • E. Franche-Comté
    Franche-Comté is a historical region in eastern France bordering Switzerland, known for its mountainous Jura landscapes, distinctive cheeses like Comté, and a past marked by shifting control between France and the Habsburgs.
  • 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_69d6aaddeaa8819088b30ef7b50598c9 completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d8014c820c81908538ba4a08e13230 completed April 9, 2026, 7:43 p.m.
NED1 Entity disambiguation (via context triple) batch_69e5b834cee48190ac09c1e1df4c12d0 completed April 20, 2026, 5:23 a.m.
Created at: April 8, 2026, 9:34 p.m.