Triple

T9749176
Position Surface form Disambiguated ID Type / Status
Subject Indre E236394 entity
Predicate borderedBy P224 FINISHED
Object Cher department E301616 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: Cher department | Statement: [Indre, borderedBy, Cher department]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Cher department
Context triple: [Indre, borderedBy, Cher department]
  • A. Cher department chosen
    Cher department is an administrative division in central France, known for its historic city of Bourges and its location within the Centre-Val de Loire region.
  • B. Aude department
    The Aude department is an administrative region in southern France known for its historic towns, vineyards, and proximity to the Mediterranean coast.
  • C. Ain department
    Ain department is an administrative region in eastern France known for its diverse landscapes, historic towns, and proximity to both the Alps and the Swiss border.
  • D. Gard department
    Gard department is an administrative region in southern France known for its Mediterranean landscapes, historic Roman sites such as the Pont du Gard, and the city of Nîmes.
  • E. Eure department
    The Eure department is an administrative region in northern France’s Normandy known for its rural landscapes, historic towns, and cultural sites such as Claude Monet’s garden at Giverny.
  • 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_69ca84d4eddc8190996fec1417d2bae8 completed March 30, 2026, 2:12 p.m.
NER Named-entity recognition batch_69cd9f6a2f8c8190a6f6af6587ee90b8 completed April 1, 2026, 10:42 p.m.
NED1 Entity disambiguation (via context triple) batch_69d1b01678f88190900a941b9d111c58 completed April 5, 2026, 12:43 a.m.
Created at: March 30, 2026, 8:23 p.m.