Triple

T1279776
Position Surface form Disambiguated ID Type / Status
Subject Haslemere E27296 entity
Predicate hasTwinTown P919 FINISHED
Object Bernay
Bernay is a historic market town in the Normandy region of northern France, known for its medieval architecture and traditional Norman character.
 E234308 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: Bernay | Statement: [Haslemere, hasTwinTown, Bernay]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Bernay
Context triple: [Haslemere, hasTwinTown, Bernay]
  • A. Lancy
    Lancy is a suburban municipality in western Switzerland that forms part of the urban area of Geneva.
  • B. Besançon
    Besançon is a historic city in eastern France, known for its well-preserved Vauban fortifications, rich cultural heritage, and role as a regional administrative and educational center.
  • C. Vandoeuvres
    Vandoeuvres is a small, affluent residential municipality located near the city of Geneva in western Switzerland.
  • D. Bourg-en-Bresse
    Bourg-en-Bresse is a historic town in eastern France known as the capital of the Ain department, noted for its Renaissance architecture and the royal monastery of Brou.
  • E. Mulhouse
    Mulhouse is an industrial city in northeastern France near the Swiss and German borders, known for its textile heritage and major technical museums.
  • 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: Bernay
Triple: [Haslemere, hasTwinTown, Bernay]
Generated description
Bernay is a historic market town in the Normandy region of northern France, known for its medieval architecture and traditional Norman character.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Bernay
Target entity description: Bernay is a historic market town in the Normandy region of northern France, known for its medieval architecture and traditional Norman character.
  • A. Lancy
    Lancy is a suburban municipality in western Switzerland that forms part of the urban area of Geneva.
  • B. Besançon
    Besançon is a historic city in eastern France, known for its well-preserved Vauban fortifications, rich cultural heritage, and role as a regional administrative and educational center.
  • C. Vandoeuvres
    Vandoeuvres is a small, affluent residential municipality located near the city of Geneva in western Switzerland.
  • D. Bourg-en-Bresse
    Bourg-en-Bresse is a historic town in eastern France known as the capital of the Ain department, noted for its Renaissance architecture and the royal monastery of Brou.
  • E. Mulhouse
    Mulhouse is an industrial city in northeastern France near the Swiss and German borders, known for its textile heritage and major technical museums.
  • 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_69a496d3710c8190955dee8bc0dacb50 completed March 1, 2026, 7:43 p.m.
NER Named-entity recognition batch_69a4c092eb688190bf42bbd59e4ff289 completed March 1, 2026, 10:41 p.m.
NED1 Entity disambiguation (via context triple) batch_69ae3037944c8190b2ced5f5ca539260 completed March 9, 2026, 2:28 a.m.
NEDg Description generation batch_69ae3149922c819085fb2af51d53304c completed March 9, 2026, 2:32 a.m.
NED2 Entity disambiguation (via description) batch_69ae31a67eb08190a3ef64e83301fabc completed March 9, 2026, 2:34 a.m.
Created at: March 1, 2026, 7:50 p.m.