Triple

T29525911
Position Surface form Disambiguated ID Type / Status
Subject Michael Nyman Band E749067 entity
Predicate collaboratesWith P37 FINISHED
Object Michael Nyman Orchestra
The Michael Nyman Orchestra is a contemporary classical ensemble founded by composer Michael Nyman, known for performing his minimalist and film music scores.
E1873460 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: Michael Nyman Orchestra | Statement: [Michael Nyman Band, collaboratesWith, Michael Nyman Orchestra]
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: Michael Nyman Orchestra
Triple: [Michael Nyman Band, collaboratesWith, Michael Nyman Orchestra]
Generated description
The Michael Nyman Orchestra is a contemporary classical ensemble founded by composer Michael Nyman, known for performing his minimalist and film music scores.

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_69f0bd46d99c81908ba9d01cc1dbef7d completed April 28, 2026, 1:59 p.m.
NER Named-entity recognition batch_69f66c9c43c081908f30ae295f032c95 completed May 2, 2026, 9:29 p.m.
NED1 Entity disambiguation (via context triple) batch_6a262d5c5e1c8190b1af673f4047ca12 completed June 8, 2026, 2:47 a.m.
NEDg Description generation batch_6a2631dbbb548190b25d75542a304887 completed June 8, 2026, 3:07 a.m.
NED2 Entity disambiguation (via description) batch_6a2635be200c8190a1b9b728f386f793 completed June 8, 2026, 3:23 a.m.
Created at: April 28, 2026, 4:46 p.m.