Triple

T31632035
Position Surface form Disambiguated ID Type / Status
Subject Alexandra Styron E807192 entity
Predicate notableWork P4 FINISHED
Object Reading My Father
"Reading My Father" is a memoir by Alexandra Styron that explores the life, career, and struggles of her father, the acclaimed novelist William Styron.
 E1970779 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: Reading My Father | Statement: [Alexandra Styron, notableWork, Reading My Father]
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: Reading My Father
Triple: [Alexandra Styron, notableWork, Reading My Father]
Generated description
"Reading My Father" is a memoir by Alexandra Styron that explores the life, career, and struggles of her father, the acclaimed novelist William Styron.

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_69f348d892948190915f8facacb9568c completed April 30, 2026, 12:19 p.m.
NER Named-entity recognition batch_69f6a8e5799c8190900640d78b1c5990 completed May 3, 2026, 1:46 a.m.
NED1 Entity disambiguation (via context triple) batch_6a2b79d68d448190b3c64dad67831108 completed June 12, 2026, 3:15 a.m.
NEDg Description generation batch_6a2b7a9bed2881908e5ca2d02afeaeff completed June 12, 2026, 3:18 a.m.
NED2 Entity disambiguation (via description) batch_6a2b7b3574f881908e98863d147351de completed June 12, 2026, 3:21 a.m.
Created at: April 30, 2026, 10:45 p.m.