Triple
T12367430
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Delay-Tolerant Networking Research Group |
E294907
|
entity |
| Predicate | relatedTo |
P37
|
FINISHED |
| Object |
Delay-Tolerant Networking architecture
Delay-Tolerant Networking architecture is a network design framework that enables reliable data communication in environments with intermittent connectivity, long delays, or high error rates, such as space, rural, or disaster-response networks.
|
E294907
|
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: Delay-Tolerant Networking architecture | Statement: [Delay-Tolerant Networking Research Group, relatedTo, Delay-Tolerant Networking architecture]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Delay-Tolerant Networking architecture Context triple: [Delay-Tolerant Networking Research Group, relatedTo, Delay-Tolerant Networking architecture]
-
A.
Delay-Tolerant Networking Research Group
The Delay-Tolerant Networking Research Group is an IRTF working group that develops architectures and protocols for communication in challenged or intermittently connected networks, such as deep space, disaster, or mobile ad hoc environments.
-
B.
Network-in-Network architecture
Network-in-Network architecture is a convolutional neural network design that replaces traditional linear convolution layers with micro multilayer perceptrons (MLPs) to enhance feature abstraction and model expressiveness.
-
C.
Time-Sensitive Networking
Time-Sensitive Networking is a set of IEEE 802 Ethernet standards that enable deterministic, low-latency, and highly reliable communication for real-time applications such as industrial automation, automotive, and professional audio/video.
-
D.
Time-Triggered Architecture
Time-Triggered Architecture is a deterministic, time-driven computing and communication framework used in safety-critical real-time systems to ensure predictable and reliable operation.
-
E.
Communication Nets: Stochastic Message Flow and Delay
"Communication Nets: Stochastic Message Flow and Delay" is a foundational book in queueing theory and computer networking that rigorously analyzes message traffic and delays in communication networks.
- 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: Delay-Tolerant Networking architecture Triple: [Delay-Tolerant Networking Research Group, relatedTo, Delay-Tolerant Networking architecture]
Generated description
Delay-Tolerant Networking architecture is a network design framework that enables reliable data communication in environments with intermittent connectivity, long delays, or high error rates, such as space, rural, or disaster-response networks.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Delay-Tolerant Networking architecture Target entity description: Delay-Tolerant Networking architecture is a network design framework that enables reliable data communication in environments with intermittent connectivity, long delays, or high error rates, such as space, rural, or disaster-response networks.
-
A.
Delay-Tolerant Networking Research Group
chosen
The Delay-Tolerant Networking Research Group is an IRTF working group that develops architectures and protocols for communication in challenged or intermittently connected networks, such as deep space, disaster, or mobile ad hoc environments.
-
B.
Network-in-Network architecture
Network-in-Network architecture is a convolutional neural network design that replaces traditional linear convolution layers with micro multilayer perceptrons (MLPs) to enhance feature abstraction and model expressiveness.
-
C.
Time-Sensitive Networking
Time-Sensitive Networking is a set of IEEE 802 Ethernet standards that enable deterministic, low-latency, and highly reliable communication for real-time applications such as industrial automation, automotive, and professional audio/video.
-
D.
Time-Triggered Architecture
Time-Triggered Architecture is a deterministic, time-driven computing and communication framework used in safety-critical real-time systems to ensure predictable and reliable operation.
-
E.
Communication Nets: Stochastic Message Flow and Delay
"Communication Nets: Stochastic Message Flow and Delay" is a foundational book in queueing theory and computer networking that rigorously analyzes message traffic and delays in communication networks.
- F. None of above.
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_69d6ab6d8a4081908636601e69ddf262 |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d93fa502988190ba170dee90d9f394 |
completed | April 10, 2026, 6:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f62abdad1c8190b083791d60138f2a |
completed | May 2, 2026, 4:47 p.m. |
| NEDg | Description generation | batch_69f62be4de888190aac94d441748d295 |
completed | May 2, 2026, 4:52 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f62d4d0b8881908aa6b67db7d14609 |
completed | May 2, 2026, 4:58 p.m. |
Created at: April 8, 2026, 9:54 p.m.