Triple
T3236068
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rhine–Main–Danube Canal |
E67857
|
entity |
| Predicate | maximumVesselTonnage |
P45572
|
FINISHED |
| Object | about 3000 tonnes |
—
|
LITERAL 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: about 3000 tonnes | Statement: [Rhine–Main–Danube Canal, maximumVesselTonnage, about 3000 tonnes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: maximumVesselTonnage Context triple: [Rhine–Main–Danube Canal, maximumVesselTonnage, about 3000 tonnes]
-
A.
maxVesselTonnage
chosen
Indicates the maximum tonnage capacity that a vessel is allowed or designed to carry.
-
B.
maximumVesselType
Indicates the highest or largest class, size, or category of vessel that is allowed, applicable, or associated in a given context.
-
C.
maximumShipBeam
Indicates the greatest allowable or observed width of a ship across its widest point.
-
D.
maximumVesselDraft
Indicates the greatest depth a vessel can safely extend below the waterline, typically limiting where it can navigate or dock.
-
E.
maximumVesselLength
Indicates the greatest allowable or observed length of a vessel in a given context or constraint.
- F. None of above.
Provenance (3 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_69ad858d27348190abb61c280b4c86a9 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69adaee05d34819095dbce4db6ac8613 |
completed | March 8, 2026, 5:16 p.m. |
| PD | Predicate disambiguation | batch_69ada4159e0481908cbbdd750f5e08c7 |
completed | March 8, 2026, 4:30 p.m. |
Created at: March 8, 2026, 3:08 p.m.