Triple

T34426119
Position Surface form Disambiguated ID Type / Status
Subject Richard Russo E883673 entity
Predicate notableWork P4 FINISHED
Object That Old Cape Magic
That Old Cape Magic is a comic novel by Pulitzer Prize–winning author Richard Russo that explores family, marriage, and midlife crisis over two eventful weddings.
 E2094734 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: That Old Cape Magic | Statement: [Richard Russo, notableWork, That Old Cape Magic]
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: That Old Cape Magic
Triple: [Richard Russo, notableWork, That Old Cape Magic]
Generated description
That Old Cape Magic is a comic novel by Pulitzer Prize–winning author Richard Russo that explores family, marriage, and midlife crisis over two eventful weddings.

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_69f349c3dd2c819092cc9e64809f4a42 completed April 30, 2026, 12:23 p.m.
NER Named-entity recognition batch_69f718e00dc88190994c42476239c27c completed May 3, 2026, 9:44 a.m.
NED1 Entity disambiguation (via context triple) batch_6a370ddc10e88190b6d5e7eb6fb52f01 completed June 20, 2026, 10:02 p.m.
NEDg Description generation batch_6a370f7561048190a9789a707d241fa1 completed June 20, 2026, 10:08 p.m.
NED2 Entity disambiguation (via description) batch_6a370fd563e4819098a0cc75d251435b completed June 20, 2026, 10:10 p.m.
Created at: May 1, 2026, 2 a.m.