Triple

T24944645
Position Surface form Disambiguated ID Type / Status
Subject Beryl Bainbridge E624152 entity
Predicate notableWork P4 FINISHED
Object Every Man for Himself
Every Man for Himself is a historical novel by Beryl Bainbridge that reimagines the final days aboard the Titanic through the eyes of a young, privileged passenger.
 E1656333 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: Every Man for Himself | Statement: [Beryl Bainbridge, notableWork, Every Man for Himself]
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: Every Man for Himself
Triple: [Beryl Bainbridge, notableWork, Every Man for Himself]
Generated description
Every Man for Himself is a historical novel by Beryl Bainbridge that reimagines the final days aboard the Titanic through the eyes of a young, privileged passenger.

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_69e2ff22e4c48190a0444b5a044f14e8 completed April 18, 2026, 3:48 a.m.
NER Named-entity recognition batch_69f423dd366081908c3cf86c20c7ec5f completed May 1, 2026, 3:54 a.m.
NED1 Entity disambiguation (via context triple) batch_6a103345eff48190bf5fdbe7be0b5834 completed May 22, 2026, 10:43 a.m.
NEDg Description generation batch_6a10341fe7448190815cb4db09d3f298 completed May 22, 2026, 10:46 a.m.
NED2 Entity disambiguation (via description) batch_6a1034c45fb88190865f904fd8e766b3 completed May 22, 2026, 10:49 a.m.
Created at: April 18, 2026, 5:53 a.m.