Triple

T9505404
Position Surface form Disambiguated ID Type / Status
Subject Sarthe E229255 entity
Predicate borders P224 FINISHED
Object Maine-et-Loire E231649 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: Maine-et-Loire | Statement: [Sarthe, borders, Maine-et-Loire]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Maine-et-Loire
Context triple: [Sarthe, borders, Maine-et-Loire]
  • A. Maine-et-Loire chosen
    Maine-et-Loire is a department in western France known for its historic towns, châteaux, and vineyards along the Loire River.
  • B. Mayenne
    Mayenne is a river in western France that flows through the regions of Normandy and Pays de la Loire before joining other waterways to form the Loire basin.
  • C. Mayenne
    Mayenne is a department in northwestern France known for its rural landscapes, historic towns, and location within the former province of Maine.
  • D. Loire-Atlantique
    Loire-Atlantique is a department in western France on the Atlantic coast, known for its capital Nantes and its historic and maritime heritage.
  • E. Deux-Sèvres
    Deux-Sèvres is a department in western France known for its rural landscapes, historic towns such as Niort, and location within the Nouvelle-Aquitaine region.
  • 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_69ca847611c48190a28c028644198c75 completed March 30, 2026, 2:11 p.m.
NER Named-entity recognition batch_69cd9850fe6c8190a5a96cfae12562c6 completed April 1, 2026, 10:12 p.m.
NED1 Entity disambiguation (via context triple) batch_69d14c09b9f88190b279335b5289defb completed April 4, 2026, 5:36 p.m.
Created at: March 30, 2026, 7:57 p.m.