Triple

T22571750
Position Surface form Disambiguated ID Type / Status
Subject Karlsruher SC E558089 entity
Predicate notableFormerPlayer P304 FINISHED
Object Thorsten Fink
Thorsten Fink is a former German professional footballer and current football manager best known for his midfield career at Bayern Munich and subsequent coaching roles across Europe and Asia.
E2157396 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: Thorsten Fink | Statement: [Karlsruher SC, notableFormerPlayer, Thorsten Fink]
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: Thorsten Fink
Triple: [Karlsruher SC, notableFormerPlayer, Thorsten Fink]
Generated description
Thorsten Fink is a former German professional footballer and current football manager best known for his midfield career at Bayern Munich and subsequent coaching roles across Europe and Asia.

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_69e11e5ae4ac8190b1f503457603d969 completed April 16, 2026, 5:37 p.m.
NER Named-entity recognition batch_69f15fae1ed881909430769a0015c39c completed April 29, 2026, 1:32 a.m.
NED1 Entity disambiguation (via context triple) batch_6a389bf2d60c8190937c8cb5940a003a completed June 22, 2026, 2:20 a.m.
NEDg Description generation batch_6a389c96694c8190869042074cd2f123 completed June 22, 2026, 2:23 a.m.
NED2 Entity disambiguation (via description) batch_6a389d7b23748190993070e1405d79de completed June 22, 2026, 2:27 a.m.
Created at: April 16, 2026, 8:52 p.m.