Triple

T31201764
Position Surface form Disambiguated ID Type / Status
Subject National Theatre (Budapest) E795493 entity
Predicate hasArtisticDirector P255 FINISHED
Object Attila Vidnyánszky
Attila Vidnyánszky is a Hungarian theatre and opera director known for his influential and often controversial leadership in Hungary’s contemporary theatrical scene.
E2092398 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: Attila Vidnyánszky | Statement: [National Theatre (Budapest), hasArtisticDirector, Attila Vidnyánszky]
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: Attila Vidnyánszky
Triple: [National Theatre (Budapest), hasArtisticDirector, Attila Vidnyánszky]
Generated description
Attila Vidnyánszky is a Hungarian theatre and opera director known for his influential and often controversial leadership in Hungary’s contemporary theatrical scene.

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_69f224d8c6608190b7882466521f62be completed April 29, 2026, 3:33 p.m.
NER Named-entity recognition batch_69f69bc2ca808190876e5cb05012dbfc completed May 3, 2026, 12:50 a.m.
NED1 Entity disambiguation (via context triple) batch_6a3704784b008190bb4a9f3c934fb2ca completed June 20, 2026, 9:22 p.m.
NEDg Description generation batch_6a370509a5b48190b19b2e0045cb5e2b completed June 20, 2026, 9:24 p.m.
NED2 Entity disambiguation (via description) batch_6a37057f47a48190aa262a6e2f5ba235 completed June 20, 2026, 9:26 p.m.
Created at: April 29, 2026, 9:09 p.m.