Triple

T28469586

Position	Surface form	Disambiguated ID	Type / Status
Subject	Remington Arms	`E720395`	entity
Predicate	notableProduct	`P1448`	FINISHED
Object	Remington Model 700 The Remington Model 700 is a widely used American bolt-action rifle renowned for its accuracy, reliability, and extensive use in hunting, sport shooting, and military and law enforcement applications.	`E1818731`	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: Remington Model 700 | Statement: [Remington Arms, notableProduct, Remington Model 700]

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: Remington Model 700
Triple: [Remington Arms, notableProduct, Remington Model 700]

Generated description

The Remington Model 700 is a widely used American bolt-action rifle renowned for its accuracy, reliability, and extensive use in hunting, sport shooting, and military and law enforcement applications.

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_69f01a58a67c819097936d9e8da8d6e6`	completed	April 28, 2026, 2:24 a.m.
NER	Named-entity recognition	`batch_69f64ee0c2788190a94a04ad1902fd5e`	completed	May 2, 2026, 7:22 p.m.
NED1	Entity disambiguation (via context triple)	`batch_6a164196d8588190a4418e37a1f62d54`	completed	May 27, 2026, 12:57 a.m.
NEDg	Description generation	`batch_6a1642b38cfc81909cdd508d4f2969b7`	completed	May 27, 2026, 1:02 a.m.
NED2	Entity disambiguation (via description)	`batch_6a16434f165c819081ea70b81354a508`	completed	May 27, 2026, 1:05 a.m.

Created at: April 28, 2026, 2:47 a.m.