Triple

T27638464
Position Surface form Disambiguated ID Type / Status
Subject Alcor Life Extension Foundation E696526 entity
Predicate foundedBy P104 FINISHED
Object Fred Chamberlain
Fred Chamberlain was a pioneering cryonics advocate and co-founder of the Alcor Life Extension Foundation, known for his work promoting life extension through cryopreservation.
E1783619 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: Fred Chamberlain | Statement: [Alcor Life Extension Foundation, foundedBy, Fred Chamberlain]
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: Fred Chamberlain
Triple: [Alcor Life Extension Foundation, foundedBy, Fred Chamberlain]
Generated description
Fred Chamberlain was a pioneering cryonics advocate and co-founder of the Alcor Life Extension Foundation, known for his work promoting life extension through cryopreservation.

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_69ef5909f3848190805f35b76833e722 completed April 27, 2026, 12:39 p.m.
NER Named-entity recognition batch_69f6318e4c9c8190960df19eed7546fa completed May 2, 2026, 5:17 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12da8d4a3c8190a5d96dddf2d26972 completed May 24, 2026, 11:01 a.m.
NEDg Description generation batch_6a12dbdc81948190b273567639a905cd completed May 24, 2026, 11:07 a.m.
NED2 Entity disambiguation (via description) batch_6a12dc453c5c819094d51c46c94b5905 completed May 24, 2026, 11:08 a.m.
Created at: April 27, 2026, 2:25 p.m.