Triple

T34889706
Position Surface form Disambiguated ID Type / Status
Subject Taryn Simon E1006247 entity
Predicate notableWork P4 FINISHED
Object An Occupation of Loss
An Occupation of Loss is a large-scale performance and installation artwork by Taryn Simon that stages professional mourners from around the world to explore rituals of grief, memory, and collective loss.
 E2116090 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: An Occupation of Loss | Statement: [Taryn Simon, notableWork, An Occupation of Loss]
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: An Occupation of Loss
Triple: [Taryn Simon, notableWork, An Occupation of Loss]
Generated description
An Occupation of Loss is a large-scale performance and installation artwork by Taryn Simon that stages professional mourners from around the world to explore rituals of grief, memory, and collective loss.

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_69f76dbedb288190afe5780710847410 completed May 3, 2026, 3:46 p.m.
NER Named-entity recognition batch_69f781bd5c448190aa4789a4d22b7a93 completed May 3, 2026, 5:11 p.m.
NED1 Entity disambiguation (via context triple) batch_6a3786e022a48190b3b569c8764a194e completed June 21, 2026, 6:38 a.m.
NEDg Description generation batch_6a37878d76308190a5cd6d03c4bd7d28 completed June 21, 2026, 6:41 a.m.
NED2 Entity disambiguation (via description) batch_6a37880ce93c8190a44f3723c33cf538 completed June 21, 2026, 6:43 a.m.
Created at: May 3, 2026, 4 p.m.