Triple

T30120717
Position Surface form Disambiguated ID Type / Status
Subject Tyrolean Zugspitz Arena E765555 entity
Predicate hasSkiResort P1981 FINISHED
Object Marienberg ski area
Marienberg ski area is a family-friendly alpine skiing destination in Austria’s Tyrolean Zugspitz Arena, offering groomed slopes, lifts, and winter sports facilities amid scenic mountain surroundings.
E1904177 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: Marienberg ski area | Statement: [Tyrolean Zugspitz Arena, hasSkiResort, Marienberg ski area]
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: Marienberg ski area
Triple: [Tyrolean Zugspitz Arena, hasSkiResort, Marienberg ski area]
Generated description
Marienberg ski area is a family-friendly alpine skiing destination in Austria’s Tyrolean Zugspitz Arena, offering groomed slopes, lifts, and winter sports facilities amid scenic mountain surroundings.

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_69f2247716748190ae4f16998f49ddf1 completed April 29, 2026, 3:32 p.m.
NER Named-entity recognition batch_69f67deb0888819090b6d69cd598c34c completed May 2, 2026, 10:42 p.m.
NED1 Entity disambiguation (via context triple) batch_6a27582907048190934e86260404aa1a completed June 9, 2026, 12:02 a.m.
NEDg Description generation batch_6a275a7d33848190ba11aeb45c7e8b83 completed June 9, 2026, 12:12 a.m.
NED2 Entity disambiguation (via description) batch_6a275b11987081908ec648ce1eeceed3 completed June 9, 2026, 12:15 a.m.
Created at: April 29, 2026, 7:13 p.m.