Triple

T9821265
Position Surface form Disambiguated ID Type / Status
Subject Eure-et-Loir E238535 entity
Predicate borders P224 FINISHED
Object Sarthe E229255 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: Sarthe | Statement: [Eure-et-Loir, borders, Sarthe]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sarthe
Context triple: [Eure-et-Loir, borders, Sarthe]
  • A. Sarthe
    Sarthe 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.
  • B. Sarthe chosen
    Sarthe is a department in western France known for its capital Le Mans and the famous 24 Hours of Le Mans endurance race.
  • C. Essonne
    Essonne is a department in northern France that forms part of the Paris metropolitan region and includes a mix of suburban communities, research centers, and rural areas.
  • D. 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.
  • E. Mayenne
    Mayenne is a department in northwestern France known for its rural landscapes, historic towns, and location within the former province of Maine.
  • 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_69ca84dfde1481909f47c286d715f892 completed March 30, 2026, 2:12 p.m.
NER Named-entity recognition batch_69cdb3147ecc81908cfca84c05a367d9 completed April 2, 2026, 12:06 a.m.
NED1 Entity disambiguation (via context triple) batch_69d1cc78ffcc8190bb26a224350376dc completed April 5, 2026, 2:44 a.m.
Created at: March 30, 2026, 8:31 p.m.