Triple

T19218435
Position Surface form Disambiguated ID Type / Status
Subject Kurios – Cabinet of Curiosities E480545 entity
Predicate costumeDesigner P184 FINISHED
Object Philippe Guillotel
Philippe Guillotel is a French costume designer known for his imaginative, theatrical creations, including work for Cirque du Soleil productions.
E2047223 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: Philippe Guillotel | Statement: [Kurios – Cabinet of Curiosities, costumeDesigner, Philippe Guillotel]
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: Philippe Guillotel
Triple: [Kurios – Cabinet of Curiosities, costumeDesigner, Philippe Guillotel]
Generated description
Philippe Guillotel is a French costume designer known for his imaginative, theatrical creations, including work for Cirque du Soleil productions.

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_69d8e8cb8c348190b52075823911c869 completed April 10, 2026, 12:10 p.m.
NER Named-entity recognition batch_69e5fa3c552c8190b22844e5b7adfc50 completed April 20, 2026, 10:04 a.m.
NED1 Entity disambiguation (via context triple) batch_6a3551d887f48190b718e6dabd16a1e8 completed June 19, 2026, 2:27 p.m.
NEDg Description generation batch_6a3553aeebc081909d55bb8589c5d40b completed June 19, 2026, 2:35 p.m.
NED2 Entity disambiguation (via description) batch_6a35555f59a481909f18895e896f5a1c completed June 19, 2026, 2:42 p.m.
Created at: April 10, 2026, 1:23 p.m.