Triple

T27107965
Position Surface form Disambiguated ID Type / Status
Subject Seattle Opera E686631 entity
Predicate hasArtisticDirector P255 FINISHED
Object Christina Scheppelmann
Christina Scheppelmann is a German opera administrator and arts leader known for serving as the general director of Seattle Opera and for her extensive international experience managing major opera companies.
 E1758923 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: Christina Scheppelmann | Statement: [Seattle Opera, hasArtisticDirector, Christina Scheppelmann]
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: Christina Scheppelmann
Triple: [Seattle Opera, hasArtisticDirector, Christina Scheppelmann]
Generated description
Christina Scheppelmann is a German opera administrator and arts leader known for serving as the general director of Seattle Opera and for her extensive international experience managing major opera companies.

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_69ef148accd48190b6ed6e13a15f2a4f completed April 27, 2026, 7:47 a.m.
NER Named-entity recognition batch_69f623fef610819086a38b74d8c934b3 completed May 2, 2026, 4:19 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12480c6f848190b6a3c7799b20f02d completed May 24, 2026, 12:36 a.m.
NEDg Description generation batch_6a124a158d9c819083f116027414b72c completed May 24, 2026, 12:45 a.m.
NED2 Entity disambiguation (via description) batch_6a124ae8eb008190a504bc1eedd82b7e completed May 24, 2026, 12:48 a.m.
Created at: April 27, 2026, 8:52 a.m.