Triple

T6457584
Position Surface form Disambiguated ID Type / Status
Subject Renfe Operadora E142030 entity
Predicate operatorOf P179 FINISHED
Object AVE high-speed network services E369134 NE 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: AVE high-speed network services | Statement: [Renfe Operadora, operatorOf, AVE high-speed network services]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: AVE high-speed network services
Context triple: [Renfe Operadora, operatorOf, AVE high-speed network services]
  • A. AVE high-speed services chosen
    AVE high-speed services are Spain’s premier long-distance high-speed train operations, connecting major cities at speeds of up to 300 km/h.
  • 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. Next Generation Network architectures
    Next Generation Network architectures are advanced telecommunications frameworks that integrate voice, data, and multimedia services over a unified, packet-based IP infrastructure to enable flexible, scalable, and service-agnostic communication.
  • D. NSFNET
    NSFNET was a high-speed, federally funded backbone network that expanded and commercialized the early internet across U.S. research and educational institutions.
  • E. 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.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

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_69c008d2f91c8190a8178767a35e08fc completed March 22, 2026, 3:20 p.m.
NER Named-entity recognition batch_69c069d758508190b9c7358ef84f8169 completed March 22, 2026, 10:14 p.m.
NED1 Entity disambiguation (via context triple) batch_69c64bdef4a881908f3d7b6eefab7def completed March 27, 2026, 9:20 a.m.
Created at: March 22, 2026, 4:48 p.m.