Triple

T31456431
Position Surface form Disambiguated ID Type / Status
Subject Shelley Kagan E802460 entity
Predicate notableWork P4 FINISHED
Object How to Count Animals, More or Less
"How to Count Animals, More or Less" is a philosophical work by Shelley Kagan that examines how we should morally evaluate and compare the interests and lives of animals.
 E1962394 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: How to Count Animals, More or Less | Statement: [Shelley Kagan, notableWork, How to Count Animals, More or Less]
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: How to Count Animals, More or Less
Triple: [Shelley Kagan, notableWork, How to Count Animals, More or Less]
Generated description
"How to Count Animals, More or Less" is a philosophical work by Shelley Kagan that examines how we should morally evaluate and compare the interests and lives of animals.

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_69f348c678ac81908a2e950867619061 completed April 30, 2026, 12:19 p.m.
NER Named-entity recognition batch_69f6a14808548190afe3161c74e09c1b completed May 3, 2026, 1:13 a.m.
NED1 Entity disambiguation (via context triple) batch_6a2b078d23d8819085e8139d69b5fa31 completed June 11, 2026, 7:07 p.m.
NEDg Description generation batch_6a2b08300fd88190bf75030c150fda91 completed June 11, 2026, 7:10 p.m.
NED2 Entity disambiguation (via description) batch_6a2b088466a48190835ae7e15a620e35 completed June 11, 2026, 7:12 p.m.
Created at: April 30, 2026, 9:16 p.m.