Triple

T28739264
Position Surface form Disambiguated ID Type / Status
Subject 1989 World Figure Skating Championships E730893 entity
Predicate notableCompetitor P11706 FINISHED
Object Claudia Leistner
Claudia Leistner is a former West German figure skater who was one of the world’s leading ladies in the 1980s, winning multiple European titles and World Championship medals.
E1858157 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: Claudia Leistner | Statement: [1989 World Figure Skating Championships, notableCompetitor, Claudia Leistner]
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: Claudia Leistner
Triple: [1989 World Figure Skating Championships, notableCompetitor, Claudia Leistner]
Generated description
Claudia Leistner is a former West German figure skater who was one of the world’s leading ladies in the 1980s, winning multiple European titles and World Championship medals.

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_69f043eae0908190b28ce314686247d7 completed April 28, 2026, 5:21 a.m.
NER Named-entity recognition batch_69f657b2b1808190a9f7c80eef2efa99 completed May 2, 2026, 7:59 p.m.
NED1 Entity disambiguation (via context triple) batch_6a2588fc89d0819098d71acdad4fc873 completed June 7, 2026, 3:06 p.m.
NEDg Description generation batch_6a258d750ab48190bdf37e21cd47cc06 completed June 7, 2026, 3:25 p.m.
NED2 Entity disambiguation (via description) batch_6a25918a8da481909aa87b4b05f403d1 completed June 7, 2026, 3:43 p.m.
Created at: April 28, 2026, 6:02 a.m.