Triple

T33192483
Position Surface form Disambiguated ID Type / Status
Subject J’irai cracher sur vos tombes E849645 entity
Predicate hasSequel P1961 FINISHED
Object Et on tuera tous les affreux
Et on tuera tous les affreux is a crime novel by Boris Vian (written under the pseudonym Vernon Sullivan) that continues his blend of hardboiled pastiche, violence, and dark satire.
E2039227 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: Et on tuera tous les affreux | Statement: [J’irai cracher sur vos tombes, hasSequel, Et on tuera tous les affreux]
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: Et on tuera tous les affreux
Triple: [J’irai cracher sur vos tombes, hasSequel, Et on tuera tous les affreux]
Generated description
Et on tuera tous les affreux is a crime novel by Boris Vian (written under the pseudonym Vernon Sullivan) that continues his blend of hardboiled pastiche, violence, and dark satire.

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_69f3495e0f108190a6a7006f79f9c2c3 completed April 30, 2026, 12:21 p.m.
NER Named-entity recognition batch_69f6d9dfce8c8190b0a9ae1f0a41eb2d completed May 3, 2026, 5:15 a.m.
NED1 Entity disambiguation (via context triple) batch_6a3525d702f08190a03ce5e00d842c9a completed June 19, 2026, 11:19 a.m.
NEDg Description generation batch_6a35267306d08190be7f605084fdf4c8 completed June 19, 2026, 11:22 a.m.
NED2 Entity disambiguation (via description) batch_6a3526dd7ef881908473f30391e82cfa completed June 19, 2026, 11:24 a.m.
Created at: May 1, 2026, 1:29 a.m.