Triple

T7393935
Position Surface form Disambiguated ID Type / Status
Subject The Whole Internet User's Guide & Catalog E170573 entity
Predicate describes P264 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: [The Whole Internet User's Guide & Catalog, describes, Usenet]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Usenet
Context triple: [The Whole Internet User's Guide & Catalog, describes, 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. BBS
    BBS is the station code used to identify the Brandenburger Tor S-Bahn station in Berlin’s public transit system.
  • D. Gnus
    Gnus is a flexible and extensible message reader for news and email, tightly integrated with the Emacs text editor.
  • E. FARK
    FARK is the acronym for the Royal Cambodian Armed Forces, the national military organization responsible for Cambodia’s defense and security.
  • 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_69c68a5e2c9081909e713ce866e0060a completed March 27, 2026, 1:47 p.m.
NER Named-entity recognition batch_69c6f2263b48819089319a2a2f0d3357 completed March 27, 2026, 9:09 p.m.
NED1 Entity disambiguation (via context triple) batch_69c810f82ba08190919924b0994a2eee completed March 28, 2026, 5:33 p.m.
Created at: March 27, 2026, 3:09 p.m.