Triple

T24820182
Position Surface form Disambiguated ID Type / Status
Subject Ruth Millikan E621038 entity
Predicate notableWork P4 FINISHED
Object Varieties of Meaning
Varieties of Meaning is a philosophical work by Ruth Millikan that develops a teleosemantic account of representation and explores how meaning arises in language, thought, and biological systems.
E1653498 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: Varieties of Meaning | Statement: [Ruth Millikan, notableWork, Varieties of Meaning]
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: Varieties of Meaning
Triple: [Ruth Millikan, notableWork, Varieties of Meaning]
Generated description
Varieties of Meaning is a philosophical work by Ruth Millikan that develops a teleosemantic account of representation and explores how meaning arises in language, thought, and biological systems.

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_69e2fabfd4648190bd0e5c7f4dbb6cab completed April 18, 2026, 3:30 a.m.
NER Named-entity recognition batch_69f42297fe00819087f0b7d666b7938a completed May 1, 2026, 3:48 a.m.
NED1 Entity disambiguation (via context triple) batch_6a101c3fe7908190840e6e31eab5abbc completed May 22, 2026, 9:05 a.m.
NEDg Description generation batch_6a1027e435748190a727eb58546d634f completed May 22, 2026, 9:54 a.m.
NED2 Entity disambiguation (via description) batch_6a1028f3eb648190a16d7ad44a76aa54 completed May 22, 2026, 9:59 a.m.
Created at: April 18, 2026, 5:04 a.m.