Triple

T36301467
Position Surface form Disambiguated ID Type / Status
Subject The Secret Scripture E893824 entity
Predicate mainCharacter P1183 FINISHED
Object Dr. William Grene
Dr. William Grene is a psychiatrist in Sebastian Barry’s novel "The Secret Scripture," whose investigation into an elderly patient’s past uncovers buried histories and moral ambiguities in modern Irish life.
E2178451 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: Dr. William Grene | Statement: [The Secret Scripture, mainCharacter, Dr. William Grene]
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: Dr. William Grene
Triple: [The Secret Scripture, mainCharacter, Dr. William Grene]
Generated description
Dr. William Grene is a psychiatrist in Sebastian Barry’s novel "The Secret Scripture," whose investigation into an elderly patient’s past uncovers buried histories and moral ambiguities in modern Irish life.

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_69f76e4c1b248190b10667d0213537fe completed May 3, 2026, 3:48 p.m.
NER Named-entity recognition batch_69f7ba036b7c8190ae61f636ae7e5ba1 completed May 3, 2026, 9:11 p.m.
NED1 Entity disambiguation (via context triple) batch_6a397d802d948190b95f4d1aff327290 completed June 22, 2026, 6:22 p.m.
NEDg Description generation batch_6a3982c390f081908fa6f1e205358ca1 completed June 22, 2026, 6:45 p.m.
NED2 Entity disambiguation (via description) batch_6a3983802c3c81908b0cd37ce3a04f53 completed June 22, 2026, 6:48 p.m.
Created at: May 3, 2026, 4:09 p.m.