Triple

T22870375
Position Surface form Disambiguated ID Type / Status
Subject Ausonius E567174 entity
Predicate notableWork P4 FINISHED
Object Mosella NE NERFINISHED

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: Mosella | Statement: [Ausonius, notableWork, Mosella]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mosella
Context triple: [Ausonius, notableWork, Mosella]
  • A. Moselle
    Moselle is a department in northeastern France, bordering Germany and Luxembourg, known for its strategic location, industrial history, and mixed French-German cultural heritage.
  • B. Moselle River chosen
    The Moselle River is a major European waterway flowing through France, Luxembourg, and Germany, renowned for its scenic valleys and wine-producing regions.
  • C. Galien River
    The Galien River is a small river in southwestern Michigan that flows through Berrien County into Lake Michigan, known for its scenic wetlands and recreational opportunities.
  • D. Rhens
    Rhens is a historic town on the Rhine River in western Germany, known for its medieval role as a meeting place of the prince-electors of the Holy Roman Empire.
  • E. Amper River
    The Amper River is a Bavarian river that flows through Upper Bavaria, including the district of Freising, before joining the Isar River.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e24589d8348190b96422d13a678bc1 completed April 17, 2026, 2:36 p.m.
NER Named-entity recognition batch_69f17f04b06481909004818ec8fc5a26 completed April 29, 2026, 3:46 a.m.
Created at: April 17, 2026, 3:38 p.m.