Triple

T31240072
Position Surface form Disambiguated ID Type / Status
Subject At the Earth’s Core E796534 entity
Predicate composer P1361 FINISHED
Object Michael Vickers
Michael Vickers is a British musician and composer best known as the former guitarist and saxophonist of the 1960s pop group Manfred Mann and for his later work in film and television scoring.
 E1981605 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 Vickers | Statement: [At the Earth’s Core, composer, Michael Vickers]
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 Vickers
Triple: [At the Earth’s Core, composer, Michael Vickers]
Generated description
Michael Vickers is a British musician and composer best known as the former guitarist and saxophonist of the 1960s pop group Manfred Mann and for his later work in film and television scoring.

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_69f224db69ac81909a370adad6a7ac7c completed April 29, 2026, 3:33 p.m.
NER Named-entity recognition batch_69f69d25dd988190b893d23052802a33 completed May 3, 2026, 12:56 a.m.
NED1 Entity disambiguation (via context triple) batch_6a2e7fbb45848190b4bec5a823ffffa0 completed June 14, 2026, 10:17 a.m.
NEDg Description generation batch_6a2e802f7d2c8190aaffa40b02fb55ee completed June 14, 2026, 10:19 a.m.
NED2 Entity disambiguation (via description) batch_6a2e809327288190a871aa12778550b9 completed June 14, 2026, 10:21 a.m.
Created at: April 29, 2026, 9:11 p.m.