Triple

T33125590
Position Surface form Disambiguated ID Type / Status
Subject works of James Joyce E847711 entity
Predicate includesWork P2011 FINISHED
Object The Cat and the Devil
The Cat and the Devil is a short, whimsical tale by James Joyce, originally written as a letter to his grandson, that playfully reimagines a French legend about a mayor bargaining with the Devil to build a bridge.
E2038258 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: The Cat and the Devil | Statement: [works of James Joyce, includesWork, The Cat and the Devil]
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: The Cat and the Devil
Triple: [works of James Joyce, includesWork, The Cat and the Devil]
Generated description
The Cat and the Devil is a short, whimsical tale by James Joyce, originally written as a letter to his grandson, that playfully reimagines a French legend about a mayor bargaining with the Devil to build a bridge.

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_69f349588f088190b7c9588860f72033 completed April 30, 2026, 12:21 p.m.
NER Named-entity recognition batch_69f6d71f3d00819088efa93eac76e17b completed May 3, 2026, 5:03 a.m.
NED1 Entity disambiguation (via context triple) batch_6a351618f4cc81908c3841284f6993c3 completed June 19, 2026, 10:12 a.m.
NEDg Description generation batch_6a3516f0a8748190bb2a2e6bb7c2b7cd completed June 19, 2026, 10:16 a.m.
NED2 Entity disambiguation (via description) batch_6a35191798188190b1738ac2d2e5e2fd completed June 19, 2026, 10:25 a.m.
Created at: May 1, 2026, 1:27 a.m.