Triple

T13565824
Position Surface form Disambiguated ID Type / Status
Subject Château de Marly E324030 entity
Predicate nearbyWatercourse P8567 FINISHED
Object Seine E6962 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: Seine | Statement: [Château de Marly, nearbyWatercourse, Seine]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Seine
Context triple: [Château de Marly, nearbyWatercourse, Seine]
  • A. Amper River
    The Amper River is a Bavarian river that flows through Upper Bavaria, including the district of Freising, before joining the Isar River.
  • B. Rhine
    The Rhine is one of Europe's most important rivers, historically serving as a vital trade route and cultural boundary from the Alps through Germany to the North Sea.
  • C. River Seine chosen
    The River Seine is a major waterway in northern France that flows through the heart of Paris and is central to the city's history, culture, and landscape.
  • D. Eure River
    The Eure River is a tributary of the Seine in northern France that flows through the city of Chartres and several other towns in the Normandy and Centre-Val de Loire regions.
  • E. Meuse
    Meuse is a department in northeastern France known for its rural landscapes and significant World War I battlefields, including Verdun.
  • 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_69d8076830b48190910a902bae5888e2 completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69dbb00cecd48190a9a2caff3d424817 completed April 12, 2026, 2:45 p.m.
NED1 Entity disambiguation (via context triple) batch_69f794281fb48190882f164df1def07e completed May 3, 2026, 6:30 p.m.
Created at: April 9, 2026, 9:48 p.m.