Triple

T32286378
Position Surface form Disambiguated ID Type / Status
Subject Jules E824840 entity
Predicate adaptedFrom P1926 FINISHED
Object novel "Jules et Jim" (1953)
The 1953 novel "Jules et Jim" is a French work by Henri-Pierre Roché that portrays a decades-long love triangle and friendship set against the backdrop of early 20th-century Europe, later famously adapted into François Truffaut’s film.
 E1999784 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: novel "Jules et Jim" (1953) | Statement: [Jules, adaptedFrom, novel "Jules et Jim" (1953)]
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: novel "Jules et Jim" (1953)
Triple: [Jules, adaptedFrom, novel "Jules et Jim" (1953)]
Generated description
The 1953 novel "Jules et Jim" is a French work by Henri-Pierre Roché that portrays a decades-long love triangle and friendship set against the backdrop of early 20th-century Europe, later famously adapted into François Truffaut’s film.

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_69f349101b788190b4f14884dc7d1ed2 completed April 30, 2026, 12:20 p.m.
NER Named-entity recognition batch_69f6bccf68b0819096225377b8130beb completed May 3, 2026, 3:11 a.m.
NED1 Entity disambiguation (via context triple) batch_6a2f46ee5f34819084ac09df6b56b1b3 completed June 15, 2026, 12:27 a.m.
NEDg Description generation batch_6a2f6f4175e88190b0ed10efdeb386b3 completed June 15, 2026, 3:19 a.m.
NED2 Entity disambiguation (via description) batch_6a2f6fd64678819081fba723a6d246e0 completed June 15, 2026, 3:21 a.m.
Created at: May 1, 2026, 12:43 a.m.