Triple

T24952411
Position Surface form Disambiguated ID Type / Status
Subject Melbourne Metro Parkville station E624374 entity
Predicate designatedAs P974 FINISHED
Object Parkville station
Parkville station is a new underground railway station on Melbourne’s Metro Tunnel project, serving the Parkville health, research, and university precinct just north of the CBD.
E1676177 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: Parkville station | Statement: [Melbourne Metro Parkville station, designatedAs, Parkville 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: Parkville station
Triple: [Melbourne Metro Parkville station, designatedAs, Parkville station]
Generated description
Parkville station is a new underground railway station on Melbourne’s Metro Tunnel project, serving the Parkville health, research, and university precinct just north of the CBD.

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_69e2ff22e4c48190a0444b5a044f14e8 completed April 18, 2026, 3:48 a.m.
NER Named-entity recognition batch_69f4240169f481909dd9345b2bce7289 completed May 1, 2026, 3:54 a.m.
NED1 Entity disambiguation (via context triple) batch_6a1075b3dfec8190b6f4ac130c3c0386 completed May 22, 2026, 3:26 p.m.
NEDg Description generation batch_6a1076991b208190945d037fd9eef5f2 completed May 22, 2026, 3:30 p.m.
NED2 Entity disambiguation (via description) batch_6a1077bbf9448190bee4351dcb985c0c completed May 22, 2026, 3:35 p.m.
Created at: April 18, 2026, 5:57 a.m.