Triple

T29620544
Position Surface form Disambiguated ID Type / Status
Subject Alfred de Musset E754982 entity
Predicate notableWork P4 FINISHED
Object On ne badine pas avec l’amour
On ne badine pas avec l’amour is a 19th-century romantic drama by Alfred de Musset that explores the tragic consequences of treating love as a game.
 E1877955 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: On ne badine pas avec l’amour | Statement: [Alfred de Musset, notableWork, On ne badine pas avec l’amour]
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: On ne badine pas avec l’amour
Triple: [Alfred de Musset, notableWork, On ne badine pas avec l’amour]
Generated description
On ne badine pas avec l’amour is a 19th-century romantic drama by Alfred de Musset that explores the tragic consequences of treating love as a game.

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_69f0ef86b6ec8190a87fff07fd983b1e completed April 28, 2026, 5:33 p.m.
NER Named-entity recognition batch_69f66e24430081908f8731d6f52e0a2c completed May 2, 2026, 9:35 p.m.
NED1 Entity disambiguation (via context triple) batch_6a26616e069c81909bdd6fc5076322e1 completed June 8, 2026, 6:30 a.m.
NEDg Description generation batch_6a26654ebf70819094a616488db916f3 completed June 8, 2026, 6:46 a.m.
NED2 Entity disambiguation (via description) batch_6a2673b6ae00819088b737c48cd73020 completed June 8, 2026, 7:48 a.m.
Created at: April 28, 2026, 6:34 p.m.