Triple

T30952039
Position Surface form Disambiguated ID Type / Status
Subject La Folle de Chaillot E788570 entity
Predicate character P662 FINISHED
Object The Flower Girl
The Flower Girl is a minor yet symbolically significant character in the satirical play *La Folle de Chaillot*, representing innocence and the everyday people affected by the greed and corruption the story critiques.
E1938283 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: The Flower Girl | Statement: [La Folle de Chaillot, character, The Flower Girl]
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: The Flower Girl
Triple: [La Folle de Chaillot, character, The Flower Girl]
Generated description
The Flower Girl is a minor yet symbolically significant character in the satirical play *La Folle de Chaillot*, representing innocence and the everyday people affected by the greed and corruption the story critiques.

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_69f224c28c1881908c33b45d689f1724 completed April 29, 2026, 3:33 p.m.
NER Named-entity recognition batch_69f693448c9c81909e1ea36705ded734 completed May 3, 2026, 12:13 a.m.
NED1 Entity disambiguation (via context triple) batch_6a28e48131548190bc28fe348fee95d9 completed June 10, 2026, 4:13 a.m.
NEDg Description generation batch_6a28e635c2a08190bb751961ba8bbc50 completed June 10, 2026, 4:21 a.m.
NED2 Entity disambiguation (via description) batch_6a28e6d1dd80819088b4c72708425be2 completed June 10, 2026, 4:23 a.m.
Created at: April 29, 2026, 8:53 p.m.