Triple

T15058891
Position Surface form Disambiguated ID Type / Status
Subject Strasbourg Airport E379569 entity
Predicate serves P98 FINISHED
Object Alsace region 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 region | Statement: [Strasbourg Airport, serves, Alsace region]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Alsace region
Context triple: [Strasbourg Airport, serves, Alsace region]
  • 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. French Lorraine
    French Lorraine is a historical region in northeastern France whose culture reflects a blend of French and Germanic influences.
  • D. Jura region
    The Jura region is a mountainous area in western Switzerland and eastern France known for its limestone plateaus, dense forests, watchmaking tradition, and distinctive wines and cheeses.
  • E. 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.
  • 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_69d85cd64d108190853797a95c11cc45 completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69dedee50afc8190bf7b0f4bbe8c60a3 completed April 15, 2026, 12:42 a.m.
NED1 Entity disambiguation (via context triple) batch_69fedd2268dc8190882e5a489e0c49c2 completed May 9, 2026, 7:07 a.m.
Created at: April 10, 2026, 3:01 a.m.