Triple

T2802883
Position Surface form Disambiguated ID Type / Status
Subject Werner Koch E53185 entity
Predicate developed P73 FINISHED
Object GnuPG E61959 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: GnuPG | Statement: [Werner Koch, developed, GnuPG]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: GnuPG
Context triple: [Werner Koch, developed, GnuPG]
  • A. GNU Privacy Guard chosen
    GNU Privacy Guard is a free, open-source implementation of the OpenPGP standard used for encrypting and signing data and communications.
  • B. PGP
    PGP (Pretty Good Privacy) is an encryption program that provides cryptographic privacy and authentication for data communication, most notably for securing emails and files.
  • C. Gpg4win
    Gpg4win is a Windows software suite for email and file encryption that provides an easy-to-use implementation of OpenPGP and S/MIME.
  • D. gpgsm
    gpgsm is the GNU Privacy Guard component that handles S/MIME public key cryptography, including X.509 certificate management and related operations.
  • E. gpg-agent
    gpg-agent is a background daemon for GNU Privacy Guard that securely manages private keys and handles cryptographic operations such as signing and decryption.
  • 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_69ab495a90788190941b6917e1eca3a6 completed March 6, 2026, 9:38 p.m.
NER Named-entity recognition batch_69abde12b33481908b276760a922db9c completed March 7, 2026, 8:13 a.m.
NED1 Entity disambiguation (via context triple) batch_69b01d5d830081909b4f5c9ec6188377 completed March 10, 2026, 1:32 p.m.
Created at: March 6, 2026, 9:58 p.m.