Triple

T30201594
Position Surface form Disambiguated ID Type / Status
Subject Pierre-Jules Hetzel E767794 entity
Predicate notableWork P4 FINISHED
Object Le Diable à Paris (as editor)
Le Diable à Paris is a mid-19th-century illustrated literary collection of Parisian scenes and stories, edited by Pierre-Jules Hetzel and featuring contributions from prominent writers and artists of the time.
 E1904519 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: Le Diable à Paris (as editor) | Statement: [Pierre-Jules Hetzel, notableWork, Le Diable à Paris (as editor)]
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: Le Diable à Paris (as editor)
Triple: [Pierre-Jules Hetzel, notableWork, Le Diable à Paris (as editor)]
Generated description
Le Diable à Paris is a mid-19th-century illustrated literary collection of Parisian scenes and stories, edited by Pierre-Jules Hetzel and featuring contributions from prominent writers and artists of the time.

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_69f2247db1108190835c0727c97637c3 completed April 29, 2026, 3:32 p.m.
NER Named-entity recognition batch_69f67fc5364881908f711ee6c3489b9d completed May 2, 2026, 10:50 p.m.
NED1 Entity disambiguation (via context triple) batch_6a2758a099088190a05f39b0063f5d02 completed June 9, 2026, 12:04 a.m.
NEDg Description generation batch_6a275a7f3e7c8190bd79a2bad2e66ca1 completed June 9, 2026, 12:12 a.m.
NED2 Entity disambiguation (via description) batch_6a275e9f9f148190ab1d1fd5ebc2d6bd completed June 9, 2026, 12:30 a.m.
Created at: April 29, 2026, 7:30 p.m.