Triple

T7530880
Position Surface form Disambiguated ID Type / Status
Subject Riviera region E178018 entity
Predicate hasMunicipality P847 FINISHED
Object Clarens
Clarens is a picturesque lakeside locality in the Swiss Riviera region on the shores of Lake Geneva, known for its scenic views and mild climate.
 E669503 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: Clarens | Statement: [Riviera region, hasMunicipality, Clarens]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Clarens
Context triple: [Riviera region, hasMunicipality, Clarens]
  • A. Groblersdal
    Groblersdal is a town in South Africa’s Limpopo province known as an important agricultural center, particularly for irrigation-based farming.
  • B. Colesberg
    Colesberg is a small historic town in South Africa’s Northern Cape, known as a key stopover on the N1 highway and a gateway to the semi-arid Karoo region.
  • C. Stormberg
    Stormberg is a mountainous region in South Africa known for its high plateaus, rugged terrain, and significant geological and historical features.
  • D. Franschhoek
    Franschhoek is a picturesque South African town renowned for its wine estates, Cape Dutch architecture, and acclaimed culinary scene.
  • E. Doornfontein
    Doornfontein is an inner-city suburb of Johannesburg, South Africa, known for its mix of historic buildings, industrial areas, and educational institutions.
  • 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: Clarens
Triple: [Riviera region, hasMunicipality, Clarens]
Generated description
Clarens is a picturesque lakeside locality in the Swiss Riviera region on the shores of Lake Geneva, known for its scenic views and mild climate.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Clarens
Target entity description: Clarens is a picturesque lakeside locality in the Swiss Riviera region on the shores of Lake Geneva, known for its scenic views and mild climate.
  • A. Groblersdal
    Groblersdal is a town in South Africa’s Limpopo province known as an important agricultural center, particularly for irrigation-based farming.
  • B. Colesberg
    Colesberg is a small historic town in South Africa’s Northern Cape, known as a key stopover on the N1 highway and a gateway to the semi-arid Karoo region.
  • C. Stormberg
    Stormberg is a mountainous region in South Africa known for its high plateaus, rugged terrain, and significant geological and historical features.
  • D. Franschhoek
    Franschhoek is a picturesque South African town renowned for its wine estates, Cape Dutch architecture, and acclaimed culinary scene.
  • E. Doornfontein
    Doornfontein is an inner-city suburb of Johannesburg, South Africa, known for its mix of historic buildings, industrial areas, and educational institutions.
  • 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_69c69f2acdbc8190b5a8320168c1d0ba completed March 27, 2026, 3:15 p.m.
NER Named-entity recognition batch_69c6f8217b1c8190b3db453cee0fc4fd completed March 27, 2026, 9:35 p.m.
NED1 Entity disambiguation (via context triple) batch_69c8463e08ac8190abd4d19b58067233 completed March 28, 2026, 9:21 p.m.
NEDg Description generation batch_69c846b326088190b93a32c70bcc97ca completed March 28, 2026, 9:22 p.m.
NED2 Entity disambiguation (via description) batch_69c8479490688190bc56b5a21d779b18 completed March 28, 2026, 9:26 p.m.
Created at: March 27, 2026, 3:47 p.m.