Triple

T26482277

Position	Surface form	Disambiguated ID	Type / Status
Subject	Jim Blinn	`E664718`	entity
Predicate	authored	`P80`	FINISHED
Object	Jim Blinn’s Corner: Dirty Pixels Jim Blinn’s Corner: Dirty Pixels is a classic computer graphics article (and later book chapter) in which pioneer Jim Blinn explains the causes and visual effects of aliasing and pixel-level artifacts, along with practical techniques to reduce them.	`E1728842`	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: Jim Blinn’s Corner: Dirty Pixels | Statement: [Jim Blinn, authored, Jim Blinn’s Corner: Dirty Pixels]

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: Jim Blinn’s Corner: Dirty Pixels
Triple: [Jim Blinn, authored, Jim Blinn’s Corner: Dirty Pixels]

Generated description

Jim Blinn’s Corner: Dirty Pixels is a classic computer graphics article (and later book chapter) in which pioneer Jim Blinn explains the causes and visual effects of aliasing and pixel-level artifacts, along with practical techniques to reduce them.

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_69ee883bc85481909885f92415cbce33`	completed	April 26, 2026, 9:48 p.m.
NER	Named-entity recognition	`batch_69f612fbdb1c8190a837de40747d6b53`	completed	May 2, 2026, 3:06 p.m.
NED1	Entity disambiguation (via context triple)	`batch_6a11bb239c708190a71e423f29d527b5`	completed	May 23, 2026, 2:35 p.m.
NEDg	Description generation	`batch_6a11be5f621881908d83370dd283a10f`	completed	May 23, 2026, 2:49 p.m.
NED2	Entity disambiguation (via description)	`batch_6a11bf7e1de48190ba8ed044628d5bf7`	completed	May 23, 2026, 2:53 p.m.

Created at: April 27, 2026, 12:28 a.m.