Triple

T13312736
Position Surface form Disambiguated ID Type / Status
Subject arrondissement of Rouen E317110 entity
Predicate contains P35 FINISHED
Object Clères
Clères is a small commune in the Normandy region of northern France, known for its historic architecture and zoological park set within a landscaped estate.
 E1036061 NE FINISHED

How this triple was built (4 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: Clères | Statement: [arrondissement of Rouen, contains, Clères]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Clères
Context triple: [arrondissement of Rouen, contains, Clères]
  • A. Chassieu
    Chassieu is a commune in the Metropolis of Lyon in eastern France, known for its residential areas and proximity to the Lyon urban center.
  • B. Céligny
    Céligny is a small, affluent Swiss village on the shores of Lake Geneva, known for its picturesque setting and as the burial place of actor Richard Burton.
  • C. Chêne-Bougeries
    Chêne-Bougeries is a suburban municipality in western Switzerland, located just east of the city of Geneva in the canton of Geneva.
  • D. Bonvillars
    Bonvillars is a small Swiss municipality in the canton of Vaud, known for its vineyards and location near Lake Neuchâtel.
  • E. Libercourt
    Libercourt is a commune in the Pas-de-Calais department in northern France, historically known as a coal-mining town.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Clères
Triple: [arrondissement of Rouen, contains, Clères]
Generated description
Clères is a small commune in the Normandy region of northern France, known for its historic architecture and zoological park set within a landscaped estate.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Clères
Target entity description: Clères is a small commune in the Normandy region of northern France, known for its historic architecture and zoological park set within a landscaped estate.
  • A. Chassieu
    Chassieu is a commune in the Metropolis of Lyon in eastern France, known for its residential areas and proximity to the Lyon urban center.
  • B. Céligny
    Céligny is a small, affluent Swiss village on the shores of Lake Geneva, known for its picturesque setting and as the burial place of actor Richard Burton.
  • C. Chêne-Bougeries
    Chêne-Bougeries is a suburban municipality in western Switzerland, located just east of the city of Geneva in the canton of Geneva.
  • D. Bonvillars
    Bonvillars is a small Swiss municipality in the canton of Vaud, known for its vineyards and location near Lake Neuchâtel.
  • E. Libercourt
    Libercourt is a commune in the Pas-de-Calais department in northern France, historically known as a coal-mining town.
  • F. None of above. chosen

Provenance (5 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_69d806b40ab4819094adf6c374f4811a completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69d990f6d34c8190ba19dc2df7d42c22 completed April 11, 2026, 12:08 a.m.
NED1 Entity disambiguation (via context triple) batch_69f7266a316c81908361acc75581f211 completed May 3, 2026, 10:41 a.m.
NEDg Description generation batch_69f7270bf9308190a3e9427ffce0e3ee completed May 3, 2026, 10:44 a.m.
NED2 Entity disambiguation (via description) batch_69f727d063c4819084b4990a0d759f79 completed May 3, 2026, 10:47 a.m.
Created at: April 9, 2026, 9:29 p.m.