Triple

T10587392
Position Surface form Disambiguated ID Type / Status
Subject Lake of Gruyère E249888 entity
Predicate crossesMunicipality P13729 FINISHED
Object Rossens
Rossens is a municipality in the canton of Fribourg in western Switzerland, known for its proximity to the Lake of Gruyère and the Rossens dam.
 E872647 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: Rossens | Statement: [Lake of Gruyère, crossesMunicipality, Rossens]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Rossens
Context triple: [Lake of Gruyère, crossesMunicipality, Rossens]
  • A. Robertsen
    Robertsen is a surname of likely Scandinavian origin, functioning as a patronymic variant of the name Roberts.
  • B. Rossosh
    Rossosh is a town in Voronezh Oblast, Russia, known as a regional center in the country’s southwest.
  • C. Raskens
    Raskens is a novel by Swedish author Vilhelm Moberg that portrays the harsh life and inner struggles of a 19th-century Swedish soldier and farmer.
  • D. Gyllensten
    Gyllensten is a Swedish surname most notably associated with Lars Gyllensten, a prominent author and former member of the Swedish Academy.
  • E. Roesler
    Roesler is a German-language surname associated with figures such as the 19th-century jurist and economist Hermann Roesler.
  • 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: Rossens
Triple: [Lake of Gruyère, crossesMunicipality, Rossens]
Generated description
Rossens is a municipality in the canton of Fribourg in western Switzerland, known for its proximity to the Lake of Gruyère and the Rossens dam.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Rossens
Target entity description: Rossens is a municipality in the canton of Fribourg in western Switzerland, known for its proximity to the Lake of Gruyère and the Rossens dam.
  • A. Robertsen
    Robertsen is a surname of likely Scandinavian origin, functioning as a patronymic variant of the name Roberts.
  • B. Rossosh
    Rossosh is a town in Voronezh Oblast, Russia, known as a regional center in the country’s southwest.
  • C. Raskens
    Raskens is a novel by Swedish author Vilhelm Moberg that portrays the harsh life and inner struggles of a 19th-century Swedish soldier and farmer.
  • D. Gyllensten
    Gyllensten is a Swedish surname most notably associated with Lars Gyllensten, a prominent author and former member of the Swedish Academy.
  • E. Roesler
    Roesler is a German-language surname associated with figures such as the 19th-century jurist and economist Hermann Roesler.
  • 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_69d381c9d3d48190a29ee491e1696a0e completed April 6, 2026, 9:50 a.m.
NER Named-entity recognition batch_69d5276b0ae48190b2935230363239e0 completed April 7, 2026, 3:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69d94b8b1b708190865e428128f98720 completed April 10, 2026, 7:12 p.m.
NEDg Description generation batch_69d94d68f39c8190bc7ea90237a5bf5f completed April 10, 2026, 7:20 p.m.
NED2 Entity disambiguation (via description) batch_69d9522d68b88190a63acb6d657168b4 completed April 10, 2026, 7:40 p.m.
Created at: April 6, 2026, 12:39 p.m.