Triple

T25602805

Position	Surface form	Disambiguated ID	Type / Status
Subject	Slepian–Wolf coding theorem	`E641830`	entity
Predicate	hasApplicationIn	`P14571`	FINISHED
Object	Wyner–Ziv coding Wyner–Ziv coding is a lossy source coding scheme with side information available only at the decoder, extending rate–distortion theory to scenarios where correlated data can be exploited during decoding.	`E1686489`	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: Wyner–Ziv coding | Statement: [Slepian–Wolf coding theorem, hasApplicationIn, Wyner–Ziv coding]

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: Wyner–Ziv coding
Triple: [Slepian–Wolf coding theorem, hasApplicationIn, Wyner–Ziv coding]

Generated description

Wyner–Ziv coding is a lossy source coding scheme with side information available only at the decoder, extending rate–distortion theory to scenarios where correlated data can be exploited during decoding.

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_69e75dc6ccf081908d49578fd36a76d5`	completed	April 21, 2026, 11:21 a.m.
NER	Named-entity recognition	`batch_69f5f9a7d7d881909014fdf3746f981b`	completed	May 2, 2026, 1:18 p.m.
NED1	Entity disambiguation (via context triple)	`batch_6a10b772c92481909ec1a4202792eb35`	completed	May 22, 2026, 8:07 p.m.
NEDg	Description generation	`batch_6a10b8265e8c8190817bca20ada4c82a`	completed	May 22, 2026, 8:10 p.m.
NED2	Entity disambiguation (via description)	`batch_6a10b97dedd48190858687f050f15f7b`	completed	May 22, 2026, 8:15 p.m.

Created at: April 21, 2026, 4:36 p.m.