Triple

T30760361
Position Surface form Disambiguated ID Type / Status
Subject John D. Voelker E783216 entity
Predicate wrote P2831 FINISHED
Object Hornstein’s Boy
Hornstein’s Boy is a novel by American author and judge John D. Voelker (also known as Robert Traver), best known for its portrayal of small-town life and the legal world in Michigan’s Upper Peninsula.
E1930900 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: Hornstein’s Boy | Statement: [John D. Voelker, wrote, Hornstein’s Boy]
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: Hornstein’s Boy
Triple: [John D. Voelker, wrote, Hornstein’s Boy]
Generated description
Hornstein’s Boy is a novel by American author and judge John D. Voelker (also known as Robert Traver), best known for its portrayal of small-town life and the legal world in Michigan’s Upper Peninsula.

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_69f224b047f48190b4f5efeb7ee97b37 completed April 29, 2026, 3:33 p.m.
NER Named-entity recognition batch_69f68f9b56988190a95f2706bb6b3217 completed May 2, 2026, 11:58 p.m.
NED1 Entity disambiguation (via context triple) batch_6a28b092f4708190bc758b6cb8bebbc5 completed June 10, 2026, 12:32 a.m.
NEDg Description generation batch_6a28b23178f08190976709c4f5806660 completed June 10, 2026, 12:39 a.m.
NED2 Entity disambiguation (via description) batch_6a28b2e6bb048190884738834cd67238 completed June 10, 2026, 12:42 a.m.
Created at: April 29, 2026, 8:39 p.m.