Triple

T27392928
Position Surface form Disambiguated ID Type / Status
Subject Louis Marie de Lescure E691588 entity
Predicate familyName P18 FINISHED
Object de Lescure
de Lescure is a French noble family name historically associated with royalist figures such as the Vendéan general Louis Marie de Lescure.
 E1771279 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: de Lescure | Statement: [Louis Marie de Lescure, familyName, de Lescure]
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: de Lescure
Triple: [Louis Marie de Lescure, familyName, de Lescure]
Generated description
de Lescure is a French noble family name historically associated with royalist figures such as the Vendéan general Louis Marie de Lescure.

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_69ef520386788190bc92cfcd97ebb67a completed April 27, 2026, 12:09 p.m.
NER Named-entity recognition batch_69f62cae21408190836baa6f4a1b52a2 completed May 2, 2026, 4:56 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12a7f45bc08190be2de35f45777df1 completed May 24, 2026, 7:25 a.m.
NEDg Description generation batch_6a12aa8ea0f48190a1a86e29a643c1fa completed May 24, 2026, 7:36 a.m.
NED2 Entity disambiguation (via description) batch_6a12ab4e587881908bf420c2fe77d0c8 completed May 24, 2026, 7:39 a.m.
Created at: April 27, 2026, 12:26 p.m.