Triple

T29579252
Position Surface form Disambiguated ID Type / Status
Subject Louis de Funès E753528 entity
Predicate notableWork P4 FINISHED
Object Le Gendarme en balade
Le Gendarme en balade is a 1970 French comedy film in the popular "Gendarme" series, featuring Louis de Funès as the bumbling yet endearing gendarme Ludovic Cruchot.
E1877761 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 Gendarme en balade | Statement: [Louis de Funès, notableWork, Le Gendarme en balade]
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 Gendarme en balade
Triple: [Louis de Funès, notableWork, Le Gendarme en balade]
Generated description
Le Gendarme en balade is a 1970 French comedy film in the popular "Gendarme" series, featuring Louis de Funès as the bumbling yet endearing gendarme Ludovic Cruchot.

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_69f0ef80bf8c8190ad286e99f7df0c63 completed April 28, 2026, 5:33 p.m.
NER Named-entity recognition batch_69f66d7825c08190953242afe9572940 completed May 2, 2026, 9:32 p.m.
NED1 Entity disambiguation (via context triple) batch_6a26615dbf608190ad31bfd790368ade completed June 8, 2026, 6:29 a.m.
NEDg Description generation batch_6a26658b86e88190b68b3a7d183a72e9 completed June 8, 2026, 6:47 a.m.
NED2 Entity disambiguation (via description) batch_6a266c326a7081909d55ff20b5c3b851 completed June 8, 2026, 7:16 a.m.
Created at: April 28, 2026, 6:05 p.m.