Triple

T7392554
Position Surface form Disambiguated ID Type / Status
Subject Rüsselsheim am Main E170536 entity
Predicate twinTown P1072 FINISHED
Object Évreux E585463 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: Évreux | Statement: [Rüsselsheim am Main, twinTown, Évreux]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Évreux
Context triple: [Rüsselsheim am Main, twinTown, Évreux]
  • A. Evreux chosen
    Evreux is a historic town in northern France, known for its Gothic cathedral and role as the capital of the Eure department in Normandy.
  • B. Bourges
    Bourges is a historic city in central France known for its well-preserved medieval architecture and its UNESCO-listed Gothic cathedral, Saint-Étienne.
  • C. Blois
    Blois is a historic city in central France known for its Renaissance château, picturesque setting on the Loire River, and rich royal heritage.
  • D. Melun
    Melun is a historic commune in the Île-de-France region of north-central France, known as a regional administrative center and former royal town southeast of Paris.
  • E. Alençon
    Alençon is a historic town in northwestern France renowned for its fine lace-making tradition and architectural heritage.
  • 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_69c68a5e2c9081909e713ce866e0060a completed March 27, 2026, 1:47 p.m.
NER Named-entity recognition batch_69c6f224790c819099ceb7c7ac8d00f6 completed March 27, 2026, 9:09 p.m.
NED1 Entity disambiguation (via context triple) batch_69c98f54f0908190afec4f26e96d8173 completed March 29, 2026, 8:45 p.m.
Created at: March 27, 2026, 3:09 p.m.