Triple

T32834647
Position Surface form Disambiguated ID Type / Status
Subject Thomas John Boyle E839791 entity
Predicate notableWork P4 FINISHED
Object Greasy Lake & Other Stories
Greasy Lake & Other Stories is a short story collection by American author T. Coraghessan Boyle that showcases his darkly comic, satirical explorations of contemporary American life.
E2026291 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: Greasy Lake & Other Stories | Statement: [Thomas John Boyle, notableWork, Greasy Lake & Other Stories]
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: Greasy Lake & Other Stories
Triple: [Thomas John Boyle, notableWork, Greasy Lake & Other Stories]
Generated description
Greasy Lake & Other Stories is a short story collection by American author T. Coraghessan Boyle that showcases his darkly comic, satirical explorations of contemporary American 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_69f3493f22f88190ae6dd4bc15b6cf8d completed April 30, 2026, 12:21 p.m.
NER Named-entity recognition batch_69f6ce2e90c081908e01d5368bf4497d completed May 3, 2026, 4:25 a.m.
NED1 Entity disambiguation (via context triple) batch_6a34bcf7e7a08190a0d800ecea931c3b completed June 19, 2026, 3:52 a.m.
NEDg Description generation batch_6a34bfbb3e8c8190b7b2d0d7dd3ba474 completed June 19, 2026, 4:04 a.m.
NED2 Entity disambiguation (via description) batch_6a34c025f24481909fe7cd29ac56efe5 completed June 19, 2026, 4:05 a.m.
Created at: May 1, 2026, 1:16 a.m.