Triple

T26804618
Position Surface form Disambiguated ID Type / Status
Subject SK Rapid Wien E671193 entity
Predicate chairman P377 FINISHED
Object Alexander Wrabetz
Alexander Wrabetz is an Austrian media executive and sports official known for his leadership roles, including serving as chairman of the football club SK Rapid Wien.
E1806751 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: Alexander Wrabetz | Statement: [SK Rapid Wien, chairman, Alexander Wrabetz]
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: Alexander Wrabetz
Triple: [SK Rapid Wien, chairman, Alexander Wrabetz]
Generated description
Alexander Wrabetz is an Austrian media executive and sports official known for his leadership roles, including serving as chairman of the football club SK Rapid Wien.

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_69eeb31fbd888190a82dac5822e453bc completed April 27, 2026, 12:51 a.m.
NER Named-entity recognition batch_69f61a1b6c348190a54a0ac2a0b463b5 completed May 2, 2026, 3:36 p.m.
NED1 Entity disambiguation (via context triple) batch_6a15d7719698819096c1a27507cd1b92 completed May 26, 2026, 5:25 p.m.
NEDg Description generation batch_6a15e027b5ac8190895de49f44f96e09 completed May 26, 2026, 6:02 p.m.
NED2 Entity disambiguation (via description) batch_6a15e07f86748190bedcf4ae291748b9 completed May 26, 2026, 6:03 p.m.
Created at: April 27, 2026, 4:25 a.m.