Triple

T28203184
Position Surface form Disambiguated ID Type / Status
Subject Let Them Eat Cake E716942 entity
Predicate featuresCharacter P626 FINISHED
Object Madame de Plonge
Madame de Plonge is a character in the historical novel "Let Them Eat Cake," depicted as part of the French aristocratic milieu surrounding the events of the French Revolution.
 E1807376 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: Madame de Plonge | Statement: [Let Them Eat Cake, featuresCharacter, Madame de Plonge]
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: Madame de Plonge
Triple: [Let Them Eat Cake, featuresCharacter, Madame de Plonge]
Generated description
Madame de Plonge is a character in the historical novel "Let Them Eat Cake," depicted as part of the French aristocratic milieu surrounding the events of the French Revolution.

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_69efd6b826908190857e6e7dad74ed93 completed April 27, 2026, 9:35 p.m.
NER Named-entity recognition batch_69f6430c4510819089589fec7d1a01e6 completed May 2, 2026, 6:31 p.m.
NED1 Entity disambiguation (via context triple) batch_6a15e6b778f48190a8a2f7257757a9f7 completed May 26, 2026, 6:30 p.m.
NEDg Description generation batch_6a15e78095b48190b38f875a418159a2 completed May 26, 2026, 6:33 p.m.
NED2 Entity disambiguation (via description) batch_6a15e8241cec819092154414d2bb5cad completed May 26, 2026, 6:36 p.m.
Created at: April 27, 2026, 10:33 p.m.