Triple

T38354663

Position	Surface form	Disambiguated ID	Type / Status
Subject	Rad Game Tools	`E1046292`	entity
Predicate	product	`P490`	FINISHED
Object	Granny 3D Granny 3D is a widely used 3D animation and asset integration middleware library designed to help game developers efficiently import, manage, and play back complex character animations across multiple platforms.	`E2265934`	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: Granny 3D | Statement: [Rad Game Tools, product, Granny 3D]

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: Granny 3D
Triple: [Rad Game Tools, product, Granny 3D]

Generated description

Granny 3D is a widely used 3D animation and asset integration middleware library designed to help game developers efficiently import, manage, and play back complex character animations across multiple platforms.

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_69f76e3a94fc81908edc175e8d259e80`	completed	May 3, 2026, 3:48 p.m.
NER	Named-entity recognition	`batch_69fcc7352fd88190852d1f6c31183920`	completed	May 7, 2026, 5:09 p.m.
NED1	Entity disambiguation (via context triple)	`batch_6a41a7fd71788190a4928633e49afa62`	completed	June 28, 2026, 11:02 p.m.
NEDg	Description generation	`batch_6a41a90ed25c81908dcdbb7e687394c5`	completed	June 28, 2026, 11:06 p.m.
NED2	Entity disambiguation (via description)	`batch_6a41a9aecf108190a0833bde27cb0e6a`	completed	June 28, 2026, 11:09 p.m.

Created at: May 3, 2026, 4:31 p.m.