Triple

T33034851
Position Surface form Disambiguated ID Type / Status
Subject Paul Sietsema E845277 entity
Predicate notableWork P4 FINISHED
Object At the hour of tea
"At the hour of tea" is a conceptual artwork by American artist Paul Sietsema, known for its meticulous, time-intensive craftsmanship and exploration of cultural artifacts and perception.
 E2034274 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: At the hour of tea | Statement: [Paul Sietsema, notableWork, At the hour of tea]
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: At the hour of tea
Triple: [Paul Sietsema, notableWork, At the hour of tea]
Generated description
"At the hour of tea" is a conceptual artwork by American artist Paul Sietsema, known for its meticulous, time-intensive craftsmanship and exploration of cultural artifacts and perception.

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_69f34951348c8190b56746b0a7018182 completed April 30, 2026, 12:21 p.m.
NER Named-entity recognition batch_69f6d2e947b88190a4a59706d3e6f959 completed May 3, 2026, 4:45 a.m.
NED1 Entity disambiguation (via context triple) batch_6a34e51034a88190b09e2fb61a4afe54 completed June 19, 2026, 6:43 a.m.
NEDg Description generation batch_6a34e60ea2148190aca7cc32e7d2b9d9 completed June 19, 2026, 6:47 a.m.
NED2 Entity disambiguation (via description) batch_6a34e6d01ce08190b4edfcda322afae0 completed June 19, 2026, 6:50 a.m.
Created at: May 1, 2026, 1:24 a.m.