Triple

T9381521
Position Surface form Disambiguated ID Type / Status
Subject Michelle Gisin E225794 entity
Predicate sibling P363 FINISHED
Object Marc Gisin
Marc Gisin is a Swiss former World Cup alpine ski racer known primarily as a speed specialist in downhill and super-G events.
 E804068 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: Marc Gisin | Statement: [Michelle Gisin, sibling, Marc Gisin]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Marc Gisin
Context triple: [Michelle Gisin, sibling, Marc Gisin]
  • A. Jürg Marmet
    Jürg Marmet was a Swiss mountaineer best known as one of the first Swiss climbers to reach the summit of Mount Everest during the 1956 expedition.
  • B. Daniel Baud-Bovy
    Daniel Baud-Bovy was a Swiss writer and mountaineer known for his pioneering climbs and contributions to the cultural and literary life of early 20th-century Switzerland.
  • C. Michelle Gisin
    Michelle Gisin is a Swiss alpine ski racer and Olympic champion known for her success in multiple World Cup disciplines.
  • D. Sandro Wüthrich
    Sandro Wüthrich is an individual notable enough to be recognized as a namesake of the surname Wüthrich.
  • E. Thierry Burkhard
    Thierry Burkhard is a French Army general who has served as France’s top military officer and a key figure in shaping the country’s contemporary defense policy and armed forces.
  • 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: Marc Gisin
Triple: [Michelle Gisin, sibling, Marc Gisin]
Generated description
Marc Gisin is a Swiss former World Cup alpine ski racer known primarily as a speed specialist in downhill and super-G events.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Marc Gisin
Target entity description: Marc Gisin is a Swiss former World Cup alpine ski racer known primarily as a speed specialist in downhill and super-G events.
  • A. Jürg Marmet
    Jürg Marmet was a Swiss mountaineer best known as one of the first Swiss climbers to reach the summit of Mount Everest during the 1956 expedition.
  • B. Daniel Baud-Bovy
    Daniel Baud-Bovy was a Swiss writer and mountaineer known for his pioneering climbs and contributions to the cultural and literary life of early 20th-century Switzerland.
  • C. Michelle Gisin
    Michelle Gisin is a Swiss alpine ski racer and Olympic champion known for her success in multiple World Cup disciplines.
  • D. Sandro Wüthrich
    Sandro Wüthrich is an individual notable enough to be recognized as a namesake of the surname Wüthrich.
  • E. Thierry Burkhard
    Thierry Burkhard is a French Army general who has served as France’s top military officer and a key figure in shaping the country’s contemporary defense policy and armed forces.
  • 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_69ca842e9dcc8190a264119e683cfe04 completed March 30, 2026, 2:09 p.m.
NER Named-entity recognition batch_69cd50be52248190bc7cd9deb95a1ef8 completed April 1, 2026, 5:07 p.m.
NED1 Entity disambiguation (via context triple) batch_69d139c79d008190841c40309d151e46 completed April 4, 2026, 4:18 p.m.
NEDg Description generation batch_69d13aaad7c8819094bbf5264b51af8e completed April 4, 2026, 4:22 p.m.
NED2 Entity disambiguation (via description) batch_69d13b0718308190b790730d46e70539 completed April 4, 2026, 4:23 p.m.
Created at: March 30, 2026, 7:44 p.m.