Triple

T38036313

Position	Surface form	Disambiguated ID	Type / Status
Subject	Perth suburban rail network	`E949359`	entity
Predicate	hasTerminus	`P388`	FINISHED
Object	Butler station Butler station is a northern suburban railway terminus in Perth, Western Australia, serving as a key endpoint for commuter services on the city's rail network.	`E2253456`	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: Butler station | Statement: [Perth suburban rail network, hasTerminus, Butler station]

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: Butler station
Triple: [Perth suburban rail network, hasTerminus, Butler station]

Generated description

Butler station is a northern suburban railway terminus in Perth, Western Australia, serving as a key endpoint for commuter services on the city's rail network.

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_69f76eff0bb0819084bc4e63997bd039`	completed	May 3, 2026, 3:51 p.m.
NER	Named-entity recognition	`batch_69fbc99f8414819090890f00da520d64`	completed	May 6, 2026, 11:07 p.m.
NED1	Entity disambiguation (via context triple)	`batch_6a415446f7248190931fa8016f10b0e3`	completed	June 28, 2026, 5:05 p.m.
NEDg	Description generation	`batch_6a415595562c8190b47fec2243f0157c`	completed	June 28, 2026, 5:10 p.m.
NED2	Entity disambiguation (via description)	`batch_6a41560e36508190b2b36868187f316b`	completed	June 28, 2026, 5:12 p.m.

Created at: May 3, 2026, 4:20 p.m.