Triple

T21629421

Position	Surface form	Disambiguated ID	Type / Status
Subject	Belmore, New South Wales	`E533788`	entity
Predicate	majorRoad	`P385`	FINISHED
Object	Kingsgrove Road Kingsgrove Road is a significant arterial road in Sydney’s south-west, connecting the suburb of Belmore with surrounding areas and facilitating local and through traffic.	`E1599174`	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: Kingsgrove Road | Statement: [Belmore, New South Wales, majorRoad, Kingsgrove Road]

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: Kingsgrove Road
Triple: [Belmore, New South Wales, majorRoad, Kingsgrove Road]

Generated description

Kingsgrove Road is a significant arterial road in Sydney’s south-west, connecting the suburb of Belmore with surrounding areas and facilitating local and through traffic.

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_69e0c464fba881908d0ff2ac80511ce1`	completed	April 16, 2026, 11:13 a.m.
NER	Named-entity recognition	`batch_69ef5215ae3c81909e6dedba23822970`	completed	April 27, 2026, 12:09 p.m.
NED1	Entity disambiguation (via context triple)	`batch_6a0f536597188190bc3d8548b817bbc3`	completed	May 21, 2026, 6:48 p.m.
NEDg	Description generation	`batch_6a0f5558f50c8190a268fbcde798512e`	completed	May 21, 2026, 6:56 p.m.
NED2	Entity disambiguation (via description)	`batch_6a0f56691aa08190b46a9ad2c3dce1d0`	completed	May 21, 2026, 7 p.m.

Created at: April 16, 2026, 6:34 p.m.