BART
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-16.)
BART has 29 facts recorded in Dontopedia across 5 references, with 2 live disagreements.
29 facts·19 predicates·5 sources·2 in dispute
Mostly:rdf:type(6), fare(2), currency(2)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (6)
Other subjects in dontopedia point AT this entity as a value. These are inverse relationships — e.g. "X motherOf this subject" — and answer questions the forward facts can't. Grouped by predicate.
exampleExample(1)
- Seq2seq Models
ex:seq2seq-models
isInputToIs Input to(1)
- Original Query
ex:original-query
isOutputOfIs Output of(1)
- Reformulated Query
ex:reformulated-query
mentionedExamplesMentioned Examples(1)
- Pretrained Models
ex:pretrained-models
plannedTransportPlanned Transport(1)
- User
ex:user
recommendedTransportationRecommended Transportation(1)
- Assistant
ex:assistant
Other facts (26)
The long tail: predicates that appear too rarely to warrant their own section. Filter or scroll to find a specific one. Each row links to its source.
26 facts
| Predicate | Value | Ref |
|---|---|---|
| Rdf:type | Model | [1] |
| Rdf:type | Seq2 Seq Model | [2] |
| Rdf:type | Llm Model | [3] |
| Rdf:type | Sequence to Sequence Model | [3] |
| Rdf:type | Public Transit | [4] |
| Rdf:type | Public Transport | [5] |
| Fare | 9.65 | [4] |
| Fare | 9.65 | [5] |
| Currency | USD | [4] |
| Currency | USD | [5] |
| Designed for | Sequence to Sequence Tasks | [1] |
| Can Take | Original Query | [1] |
| Can Generate | Reformulated Query | [1] |
| Subclass of | Sequence to Sequence Model | [1] |
| Is Example of | Sequence to Sequence Model | [1] |
| Instance of | Seq2seq Models | [2] |
| Is Pretrained | true | [3] |
| Route From | Sfo Airport | [4] |
| Route to | Civic Center Station | [4] |
| Trip Duration | 30 | [4] |
| Trip Duration Max | 40 | [4] |
| Convenient for | airport to city | [4] |
| Affordable | true | [4] |
| Route | SFO-Airport-to-Civic-Center | [5] |
| Stops at | Civic Center Station | [5] |
| Duration | 30-40 minutes | [5] |
Timeline
Timeline axis is valid_time — when each source says the fact was true in the world, not when Dontopedia learned about it. Retracted rows are kept for provenance; coloured stripes indicate the context kind.
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typebeam/8a3d9053-ab82-4206-8ea2-43c648648492
ex:Model
—
designedForbeam/8a3d9053-ab82-4206-8ea2-43c648648492
ex:sequence-to-sequence-tasks
—
canTakebeam/8a3d9053-ab82-4206-8ea2-43c648648492
ex:original-query
—
canGeneratebeam/8a3d9053-ab82-4206-8ea2-43c648648492
ex:reformulated-query
—
subclassOfbeam/8a3d9053-ab82-4206-8ea2-43c648648492
ex:sequence-to-sequence-model
—
isExampleOfbeam/8a3d9053-ab82-4206-8ea2-43c648648492
ex:sequence-to-sequence-model
—
typebeam/c6ef7f06-9aff-4257-8e3b-7d0cb4d24d70
ex:Seq2SeqModel
—
instanceOfbeam/c6ef7f06-9aff-4257-8e3b-7d0cb4d24d70
ex:seq2seq-models
—
typebeam/a5846ddf-c0a1-4872-b232-a7b71690ed03
ex:LLMModel
—
typebeam/a5846ddf-c0a1-4872-b232-a7b71690ed03
ex:SequenceToSequenceModel
—
labelbeam/a5846ddf-c0a1-4872-b232-a7b71690ed03
BART
—
isPretrainedbeam/a5846ddf-c0a1-4872-b232-a7b71690ed03
true
—
typelme/6a81f139-4bf6-4c88-85fc-37490028e257
ex:PublicTransit
—
labellme/6a81f139-4bf6-4c88-85fc-37490028e257
Bay Area Rapid Transit
—
routeFromlme/6a81f139-4bf6-4c88-85fc-37490028e257
ex:SFO Airport
—
routeTolme/6a81f139-4bf6-4c88-85fc-37490028e257
ex:Civic Center Station
—
farelme/6a81f139-4bf6-4c88-85fc-37490028e257
9.65
—
currencylme/6a81f139-4bf6-4c88-85fc-37490028e257
USD
—
tripDurationlme/6a81f139-4bf6-4c88-85fc-37490028e257
30
—
tripDurationMaxlme/6a81f139-4bf6-4c88-85fc-37490028e257
40
—
convenientForlme/6a81f139-4bf6-4c88-85fc-37490028e257
airport to city
—
affordablelme/6a81f139-4bf6-4c88-85fc-37490028e257
true
—
typelme/bd54b30d-a8f8-47e2-a327-39493073aeac
ex:PublicTransport
—
labellme/bd54b30d-a8f8-47e2-a327-39493073aeac
Bay Area Rapid Transit
—
routelme/bd54b30d-a8f8-47e2-a327-39493073aeac
SFO-Airport-to-Civic-Center
—
stopsAtlme/bd54b30d-a8f8-47e2-a327-39493073aeac
ex:Civic-Center-Station
—
durationlme/bd54b30d-a8f8-47e2-a327-39493073aeac
30-40 minutes
—
farelme/bd54b30d-a8f8-47e2-a327-39493073aeac
9.65
—
currencylme/bd54b30d-a8f8-47e2-a327-39493073aeac
USD
References (5)
5 references
ctx:claims/beam/8a3d9053-ab82-4206-8ea2-43c648648492- full textbeam-chunktext/plain1 KB
doc:beam/8a3d9053-ab82-4206-8ea2-43c648648492Show excerpt
Your current implementation uses `np.argmax(outputs.logits)` which suggests you are treating the reformulation as a classification problem. However, query reformulation is often better handled as a sequence-to-sequence task. Instead of clas…
ctx:claims/beam/c6ef7f06-9aff-4257-8e3b-7d0cb4d24d70- full textbeam-chunktext/plain1 KB
doc:beam/c6ef7f06-9aff-4257-8e3b-7d0cb4d24d70Show excerpt
[Turn 10407] Assistant: Certainly! To improve your query rewriting pipeline and achieve at least 92% detection accuracy for 18,000 query inputs, you can consider several enhancements. Here are some steps and suggestions: ### 1. **Use Seque…
ctx:claims/beam/a5846ddf-c0a1-4872-b232-a7b71690ed03- full textbeam-chunktext/plain1 KB
doc:beam/a5846ddf-c0a1-4872-b232-a7b71690ed03Show excerpt
[Turn 10425] Assistant: Certainly! Integrating LLM-based reformulation into your existing pipeline can significantly improve the accuracy and relevance of your query reformulations. Here's a step-by-step guide to help you incorporate LLM-ba…
ctx:claims/lme/6a81f139-4bf6-4c88-85fc-37490028e257- full textbeam-chunktext/plain14 KB
doc:beam/6a81f139-4bf6-4c88-85fc-37490028e257Show excerpt
[Session date: 2023/05/27 (Sat) 06:10] User: I'm planning a trip to San Francisco and was wondering if you could recommend some good neighborhoods to stay in. By the way, I've had a great experience with Airbnb in the past, like when I stay…
ctx:claims/lme/bd54b30d-a8f8-47e2-a327-39493073aeac- full textbeam-chunktext/plain14 KB
doc:beam/bd54b30d-a8f8-47e2-a327-39493073aeacShow excerpt
[Session date: 2023/05/25 (Thu) 03:03] User: I'm planning a trip to San Francisco and was wondering if you could recommend some good neighborhoods to stay in. By the way, I've had a great experience with Airbnb in the past, like when I stay…
See also
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