Triple

T27343167
Position Surface form Disambiguated ID Type / Status
Subject Renée Vivien E684164 entity
Predicate notableWork P4 FINISHED
Object Une femme m’apparut
"Une femme m’apparut" is a symbolist, sapphic poetry collection by French-British poet Renée Vivien, exploring themes of female desire, melancholy, and idealized love.
 E1766777 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: Une femme m’apparut | Statement: [Renée Vivien, notableWork, Une femme m’apparut]
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: Une femme m’apparut
Triple: [Renée Vivien, notableWork, Une femme m’apparut]
Generated description
"Une femme m’apparut" is a symbolist, sapphic poetry collection by French-British poet Renée Vivien, exploring themes of female desire, melancholy, and idealized love.

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_69ef1480a76481908684256ddd5bfda3 completed April 27, 2026, 7:47 a.m.
NER Named-entity recognition batch_69f62b9f5514819088c3a1e11c65a9af completed May 2, 2026, 4:51 p.m.
NED1 Entity disambiguation (via context triple) batch_6a129cc77f5c81909cc87a5e99d88685 completed May 24, 2026, 6:37 a.m.
NEDg Description generation batch_6a129e71ddc481908a3659f89b0beb0f completed May 24, 2026, 6:45 a.m.
NED2 Entity disambiguation (via description) batch_6a129fa2db9c8190be46762dee9a6394 completed May 24, 2026, 6:50 a.m.
Created at: April 27, 2026, 11:44 a.m.