Triple
T14478168
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Siemens Vectron |
E359028
|
entity |
| Predicate | operator |
P179
|
FINISHED |
| Object |
CD Cargo
ČD Cargo is a major Czech rail freight company that operates domestic and international cargo services across Europe.
|
E1100595
|
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: CD Cargo | Statement: [Siemens Vectron, operator, CD Cargo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: CD Cargo Context triple: [Siemens Vectron, operator, CD Cargo]
-
A.
DB Cargo
DB Cargo is the rail freight division of Germany’s national railway company, providing cargo transport and logistics services across Europe.
-
B.
Cargo
Cargo is an Australian post-apocalyptic horror drama film best known for its emotional story of a father trying to save his infant daughter during a zombie outbreak.
-
C.
Cargo
Cargo is Rust’s official build and dependency management tool that streamlines compiling code, managing libraries, and distributing Rust packages.
-
D.
Cargo
Cargo is a small rural town in the Central West region of New South Wales, Australia, known for its agricultural surroundings and village community.
-
E.
LOT Cargo
LOT Cargo is the air freight and cargo handling division of LOT Polish Airlines, providing logistics and cargo transport services on the carrier’s route network.
- 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: CD Cargo Triple: [Siemens Vectron, operator, CD Cargo]
Generated description
ČD Cargo is a major Czech rail freight company that operates domestic and international cargo services across Europe.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: CD Cargo Target entity description: ČD Cargo is a major Czech rail freight company that operates domestic and international cargo services across Europe.
-
A.
DB Cargo
DB Cargo is the rail freight division of Germany’s national railway company, providing cargo transport and logistics services across Europe.
-
B.
Cargo
Cargo is an Australian post-apocalyptic horror drama film best known for its emotional story of a father trying to save his infant daughter during a zombie outbreak.
-
C.
Cargo
Cargo is Rust’s official build and dependency management tool that streamlines compiling code, managing libraries, and distributing Rust packages.
-
D.
Cargo
Cargo is a small rural town in the Central West region of New South Wales, Australia, known for its agricultural surroundings and village community.
-
E.
LOT Cargo
LOT Cargo is the air freight and cargo handling division of LOT Polish Airlines, providing logistics and cargo transport services on the carrier’s route network.
- 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_69d827966698819082e140837737501d |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de9248edb48190a74eb032aeaac027 |
completed | April 14, 2026, 7:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd64a257488190818c65c1cc84c4b5 |
completed | May 8, 2026, 4:20 a.m. |
| NEDg | Description generation | batch_69fd6609ed5c8190a5d2c5fe25ea1467 |
completed | May 8, 2026, 4:26 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fd666f81d08190a0d658b5949e0201 |
completed | May 8, 2026, 4:28 a.m. |
Created at: April 10, 2026, 1:20 a.m.