Triple

T1077126
Position Surface form Disambiguated ID Type / Status
Subject Teredo E23864 entity
Predicate hasComponent P35 FINISHED
Object Teredo client E23864 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: Teredo client | Statement: [Teredo, hasComponent, Teredo client]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Teredo client
Context triple: [Teredo, hasComponent, Teredo client]
  • A. Teredo chosen
    Teredo is a tunneling protocol that enables IPv6 connectivity for devices on IPv4 networks, particularly those behind NAT.
  • B. TUN
    TUN is the three-letter ISO 3166-1 alpha-3 country code assigned to Tunisia.
  • C. NAT64
    NAT64 is a network address translation mechanism that enables IPv6-only clients to communicate with IPv4 servers by translating between the two protocol address spaces and packet formats.
  • D. TFTP
    TFTP (Trivial File Transfer Protocol) is a simple, lightweight file transfer protocol commonly used for tasks like network booting and device configuration in constrained environments.
  • E. TCP/IP
    TCP/IP is the fundamental communication protocol suite that enables data transmission and networking across the internet and most modern computer networks.
  • 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_69a493f1ddf48190a99d54b00e99f8ce completed March 1, 2026, 7:30 p.m.
NER Named-entity recognition batch_69a4b94288d88190aae4fb86236c0702 completed March 1, 2026, 10:10 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac42abc7a08190a34f5b2d393db30e completed March 7, 2026, 3:22 p.m.
Created at: March 1, 2026, 7:42 p.m.