Triple

T24898238
Position Surface form Disambiguated ID Type / Status
Subject Melbourne Underground Rail Loop E623202 entity
Predicate alsoKnownAs P39 FINISHED
Object Melbourne City Loop
Melbourne City Loop is a major underground railway system in central Melbourne that routes suburban train lines through a series of tunnels and city stations to improve capacity and connectivity.
 E1653988 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: Melbourne City Loop | Statement: [Melbourne Underground Rail Loop, alsoKnownAs, Melbourne City Loop]
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: Melbourne City Loop
Triple: [Melbourne Underground Rail Loop, alsoKnownAs, Melbourne City Loop]
Generated description
Melbourne City Loop is a major underground railway system in central Melbourne that routes suburban train lines through a series of tunnels and city stations to improve capacity and connectivity.

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_69e2fac597708190a922bf39a49ec70a completed April 18, 2026, 3:30 a.m.
NER Named-entity recognition batch_69f42349984481909377980e1ea6d471 completed May 1, 2026, 3:51 a.m.
NED1 Entity disambiguation (via context triple) batch_6a101c6ec49481908a3d785da4fd9ee3 completed May 22, 2026, 9:05 a.m.
NEDg Description generation batch_6a1028d69ba881908549776b278afb7d completed May 22, 2026, 9:58 a.m.
NED2 Entity disambiguation (via description) batch_6a1029d17e8c8190b73912ffdb8fd0f8 completed May 22, 2026, 10:02 a.m.
Created at: April 18, 2026, 5:26 a.m.