Triple

T9865332
Position Surface form Disambiguated ID Type / Status
Subject RFC 977 E239817 entity
Predicate applicationDomain P1248 FINISHED
Object Usenet E233830 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: Usenet | Statement: [RFC 977, applicationDomain, Usenet]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Usenet
Context triple: [RFC 977, applicationDomain, Usenet]
  • A. Usenet chosen
    Usenet is a worldwide distributed discussion system that predates the modern web, where users post and read messages in topic-based newsgroups.
  • B. NNTP
    NNTP (Network News Transfer Protocol) is an application-layer protocol used for reading and posting articles on Usenet newsgroups over TCP/IP networks.
  • C. UUCP
    UUCP (Unix-to-Unix Copy Program) is an early suite of computer programs and protocols used primarily on Unix systems to transfer files, email, and netnews between computers over serial lines and dial-up connections.
  • D. BBS
    BBS is the station code used to identify the Brandenburger Tor S-Bahn station in Berlin’s public transit system.
  • E. Gnus
    Gnus is a flexible and extensible message reader for news and email, tightly integrated with the Emacs text editor.
  • 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_69ca84e7506c819095cbde4ff16512bb completed March 30, 2026, 2:12 p.m.
NER Named-entity recognition batch_69cdb3ba7f288190a15ebec2cc3112c4 completed April 2, 2026, 12:09 a.m.
NED1 Entity disambiguation (via context triple) batch_69d1e44dc0b8819082294a479299814e completed April 5, 2026, 4:25 a.m.
Created at: March 30, 2026, 8:36 p.m.