Triple
T21775447
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | CargoNet |
E537560
|
entity |
| Predicate | hasShareholder |
P1553
|
FINISHED |
| Object | Green Cargo |
—
|
NE NERFINISHED |
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: Green Cargo | Statement: [CargoNet, hasShareholder, Green Cargo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Green Cargo Context triple: [CargoNet, hasShareholder, Green Cargo]
-
A.
Green Cargo
chosen
Green Cargo is a Swedish state-owned rail freight company that provides cargo transport services across Scandinavia and parts of Europe.
-
B.
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.
-
C.
MEA Cargo
MEA Cargo is the air freight division of Middle East Airlines, providing cargo transport and logistics services primarily through its operations at Beirut–Rafic Hariri International Airport.
-
D.
Cargo
Cargo is Rust’s official build and dependency management tool that streamlines compiling code, managing libraries, and distributing Rust packages.
-
E.
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.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0c470759c819094a215757113562b |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69f046291d808190b5111a8d4819909f |
completed | April 28, 2026, 5:31 a.m. |
Created at: April 16, 2026, 6:51 p.m.