Triple

T11163021
Position Surface form Disambiguated ID Type / Status
Subject Autoroute A4 E264086 entity
Predicate passesThroughDepartment P78707 FINISHED
Object Moselle E93471 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: Moselle | Statement: [Autoroute A4, passesThroughDepartment, Moselle]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Moselle
Context triple: [Autoroute A4, passesThroughDepartment, Moselle]
  • A. Moselle chosen
    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
    The Moselle River is a major European waterway flowing through France, Luxembourg, and Germany, renowned for its scenic valleys and wine-producing regions.
  • C. Saar River
    The Saar River is a major river in northeastern France and western Germany that flows through the industrial region of Saarland before joining the Moselle.
  • D. Merzig
    Merzig is a town in the Saarland region of western Germany, near the borders with France and Luxembourg.
  • E. 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.
  • 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_69d6aa9ccddc8190868998c8b7beb060 completed April 8, 2026, 7:21 p.m.
NER Named-entity recognition batch_69d7e8832fe88190a74d81f9ed547baa completed April 9, 2026, 5:57 p.m.
NED1 Entity disambiguation (via context triple) batch_69f01869e6c8819093f2768b57b183aa completed April 28, 2026, 2:16 a.m.
Created at: April 8, 2026, 9:29 p.m.