Triple
T6863530
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cranbourne line |
E158339
|
entity |
| Predicate | usesRollingStock |
P5426
|
FINISHED |
| Object |
Siemens Nexas
Siemens Nexas is a class of electric multiple unit trains used for suburban passenger services on Melbourne’s metropolitan rail network.
|
E623209
|
NE FINISHED |
How this triple was built (4 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: Siemens Nexas | Statement: [Cranbourne line, usesRollingStock, Siemens Nexas]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Siemens Nexas Context triple: [Cranbourne line, usesRollingStock, Siemens Nexas]
-
A.
Siemens Avanto
Siemens Avanto is a family of light rail and tram-train vehicles developed by Siemens for urban and regional public transport systems.
-
B.
Siemens Inspiro
Siemens Inspiro is a modern, modular metro train platform developed by Siemens for urban rapid transit systems worldwide.
-
C.
Siemens SD100
The Siemens SD100 is a light rail vehicle model built by Siemens for use on urban trolley and light rail systems such as the San Diego Trolley.
-
D.
Siemens U2
Siemens U2 is a type of light rail vehicle widely used in North American transit systems, including the San Diego Trolley, known for its articulated design and high-floor configuration.
-
E.
Siemens SD660
Siemens SD660 is a model of light rail vehicle built by Siemens for use in modern urban transit systems.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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: Siemens Nexas Triple: [Cranbourne line, usesRollingStock, Siemens Nexas]
Generated description
Siemens Nexas is a class of electric multiple unit trains used for suburban passenger services on Melbourne’s metropolitan rail network.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Siemens Nexas Target entity description: Siemens Nexas is a class of electric multiple unit trains used for suburban passenger services on Melbourne’s metropolitan rail network.
-
A.
Siemens Avanto
Siemens Avanto is a family of light rail and tram-train vehicles developed by Siemens for urban and regional public transport systems.
-
B.
Siemens Inspiro
Siemens Inspiro is a modern, modular metro train platform developed by Siemens for urban rapid transit systems worldwide.
-
C.
Siemens SD100
The Siemens SD100 is a light rail vehicle model built by Siemens for use on urban trolley and light rail systems such as the San Diego Trolley.
-
D.
Siemens U2
Siemens U2 is a type of light rail vehicle widely used in North American transit systems, including the San Diego Trolley, known for its articulated design and high-floor configuration.
-
E.
Siemens SD660
Siemens SD660 is a model of light rail vehicle built by Siemens for use in modern urban transit systems.
- F. None of above. chosen
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_69c68830cdbc8190a8301c7a9d9f651a |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d8897cf48190a2f688d21f1f6c1a |
completed | March 27, 2026, 7:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c72ff153d48190a4b0d4e403457fe8 |
completed | March 28, 2026, 1:33 a.m. |
| NEDg | Description generation | batch_69c730e882308190a3fbc61245941338 |
completed | March 28, 2026, 1:37 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c7316a9cc081908c088725cb7626e8 |
completed | March 28, 2026, 1:39 a.m. |
Created at: March 27, 2026, 2:21 p.m.