Triple

T33181871
Position Surface form Disambiguated ID Type / Status
Subject Lucien Hervé E849349 entity
Predicate child P120 FINISHED
Object Rodolf Hervé
Rodolf Hervé was a French experimental photographer and multimedia artist known for his avant-garde urban imagery and politically engaged work in the late 20th century.
 E2297004 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: Rodolf Hervé | Statement: [Lucien Hervé, child, Rodolf Hervé]
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: Rodolf Hervé
Triple: [Lucien Hervé, child, Rodolf Hervé]
Generated description
Rodolf Hervé was a French experimental photographer and multimedia artist known for his avant-garde urban imagery and politically engaged work in the late 20th century.

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_69f3495d06508190b0b7729982982cea completed April 30, 2026, 12:21 p.m.
NER Named-entity recognition batch_69f6d99dc7108190a4ea556f2a4eb95e completed May 3, 2026, 5:14 a.m.
NED1 Entity disambiguation (via context triple) batch_6a82f26382388190815892e85fdf0a85 completed Aug. 17, 2026, 11:37 a.m.
NEDg Description generation batch_6a82f2b43dec8190957d45a98322ee4a completed Aug. 17, 2026, 11:38 a.m.
NED2 Entity disambiguation (via description) batch_6a82f34e6228819085524e4e5e51a135 completed Aug. 17, 2026, 11:41 a.m.
Created at: May 1, 2026, 1:29 a.m.