Triple

T8008988
Position Surface form Disambiguated ID Type / Status
Subject RFC 857 E186435 entity
Predicate specifiesProtocol P12564 FINISHED
Object Telnet E5624 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: Telnet | Statement: [RFC 857, specifiesProtocol, Telnet]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Telnet
Context triple: [RFC 857, specifiesProtocol, Telnet]
  • A. Telnet chosen
    Telnet is a network protocol and command-line tool that allows users to remotely access and manage devices over a text-based terminal connection.
  • B. Putty
    Putty is a small rural locality in New South Wales, Australia, known for its forested landscapes and low-density population.
  • C. PuTTY
    PuTTY is a widely used free and open-source terminal emulator and network client for Windows and other platforms, supporting protocols like SSH, Telnet, and serial connections.
  • D. Terminal
    Terminal is the built-in command-line interface application for macOS that allows users to interact with the operating system using text-based commands.
  • E. Terminal
    Terminal is a 2018 neo-noir thriller film starring Margot Robbie, known for its stylized visuals and dark, twisting narrative.
  • 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_69ca82abaffc8190ab8af79cdbc31ab3 completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cb3d6f76408190a1312369521a187a completed March 31, 2026, 3:20 a.m.
NED1 Entity disambiguation (via context triple) batch_69cc569e90d48190a1bf1495496017f8 completed March 31, 2026, 11:19 p.m.
Created at: March 30, 2026, 5:19 p.m.