Triple

T24928749

Position	Surface form	Disambiguated ID	Type / Status
Subject	Sorenson Video	`E618930`	entity
Predicate	encodingSoftware	`P82559`	FINISHED
Object	Sorenson Squeeze Sorenson Squeeze is a professional video encoding and compression application widely used for preparing digital video for web, broadcast, and multimedia distribution.	`E1658504`	NE FINISHED

How this triple was built (3 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: Sorenson Squeeze | Statement: [Sorenson Video, encodingSoftware, Sorenson Squeeze]

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: Sorenson Squeeze
Triple: [Sorenson Video, encodingSoftware, Sorenson Squeeze]

Generated description

Sorenson Squeeze is a professional video encoding and compression application widely used for preparing digital video for web, broadcast, and multimedia distribution.

PD Predicate disambiguation gpt-5-mini-2025-08-07

Target predicate: encodingSoftware
Context triple: [Sorenson Video, encodingSoftware, Sorenson Squeeze]

A. encodingLibrary chosen
Indicates that one entity is the software library or tool used to encode, transform, or serialize the other entity’s data or content.
B. developedSoftware
Indicates that one entity created, programmed, or significantly contributed to the creation of a particular software system or application for another entity or purpose.
C. encodes
Indicates that one entity contains or represents the information, instructions, or structure of another in a coded or symbolic form.
D. softwareFor
Indicates that one entity is designed, intended, or used to operate on, support, or be compatible with another entity as software.
E. softwareSystem
Indicates a relationship where an entity functions as, or is classified as, a software system.
F. None of above.

Provenance (6 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_69e2fab9edd88190b86004a78a28bc20`	completed	April 18, 2026, 3:30 a.m.
NER	Named-entity recognition	`batch_69f44a417a58819081777e18dda149fd`	completed	May 1, 2026, 6:37 a.m.
NED1	Entity disambiguation (via context triple)	`batch_6a1033397e5881908eb8f4d277861a2f`	completed	May 22, 2026, 10:43 a.m.
NEDg	Description generation	`batch_6a10341f2f84819080ce00e1d48f4fa1`	completed	May 22, 2026, 10:46 a.m.
NED2	Entity disambiguation (via description)	`batch_6a103522b834819090aec1df37f496e8`	completed	May 22, 2026, 10:51 a.m.
PD	Predicate disambiguation	`batch_69f442b8479c8190a7c8e416ac9e28a0`	completed	May 1, 2026, 6:05 a.m.

Created at: April 18, 2026, 5:29 a.m.