Triple

T36809167

Position	Surface form	Disambiguated ID	Type / Status
Subject	Murwillumbah railway station	`E909539`	entity
Predicate	adjacentStationBeforeClosure	`P66132`	FINISHED
Object	Condong railway station Condong railway station was a small regional stop on the former Murwillumbah railway line in New South Wales, Australia.	`E2198398`	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: Condong railway station | Statement: [Murwillumbah railway station, adjacentStationBeforeClosure, Condong railway 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: Condong railway station
Triple: [Murwillumbah railway station, adjacentStationBeforeClosure, Condong railway station]

Generated description

Condong railway station was a small regional stop on the former Murwillumbah railway line in New South Wales, Australia.

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

Target predicate: adjacentStationBeforeClosure
Context triple: [Murwillumbah railway station, adjacentStationBeforeClosure, Condong railway station]

A. adjacentStationFormerly
Indicates that two stations were directly adjacent to each other in the past, but are no longer adjacent in the current network configuration.
B. adjacentStationOnA
Indicates that one station is directly next to another station along line A in the network.
C. adjacentToStation
Indicates that one entity is located next to or immediately beside a station.
D. precedingStation chosen
Indicates that one station is located immediately before another station in a route or sequence.
E. adjacentStationOnC
Indicates that one station is directly next to another station along line C in the network.
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_69f76e7cbbf48190891227b14d041139`	completed	May 3, 2026, 3:49 p.m.
NER	Named-entity recognition	`batch_69fd2a215d6c8190a1a428ccaee603f1`	completed	May 8, 2026, 12:11 a.m.
NED1	Entity disambiguation (via context triple)	`batch_6a3d17b65f448190af10d2786fea23f6`	completed	June 25, 2026, 11:57 a.m.
NEDg	Description generation	`batch_6a3d19aed8d88190aad7cf2a9fa8df16`	completed	June 25, 2026, 12:06 p.m.
NED2	Entity disambiguation (via description)	`batch_6a3d2c4c5d0881908569853ba7876029`	completed	June 25, 2026, 1:25 p.m.
PD	Predicate disambiguation	`batch_69fd28ef19688190bb8370f2812a43e7`	completed	May 8, 2026, 12:06 a.m.

Created at: May 3, 2026, 4:13 p.m.