Triple

T7899044
Position Surface form Disambiguated ID Type / Status
Subject Hal Finney E183402 entity
Predicate employer P7 FINISHED
Object Phil Zimmermann’s PGP team E35340 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: Phil Zimmermann’s PGP team | Statement: [Hal Finney, employer, Phil Zimmermann’s PGP team]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Phil Zimmermann’s PGP team
Context triple: [Hal Finney, employer, Phil Zimmermann’s PGP team]
  • A. GNU Privacy Guard
    GNU Privacy Guard is a free, open-source implementation of the OpenPGP standard used for encrypting and signing data and communications.
  • B. Werner Koch
    Werner Koch is a German free software developer best known as the principal author and maintainer of the GNU Privacy Guard (GnuPG) encryption software.
  • C. PGP chosen
    PGP (Pretty Good Privacy) is an encryption program that provides cryptographic privacy and authentication for data communication, most notably for securing emails and files.
  • D. Roger Dingledine
    Roger Dingledine is a computer scientist and privacy advocate best known as a co-founder and key developer of the Tor anonymity network.
  • E. Paul Vixie
    Paul Vixie is an American computer scientist and Internet pioneer best known for his influential work on the Domain Name System (DNS), including major contributions to BIND and DNS infrastructure 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_69ca828d13088190b222be7aa9f9315c completed March 30, 2026, 2:02 p.m.
NER Named-entity recognition batch_69cb3a2ae5048190a6824d34b582c366 completed March 31, 2026, 3:06 a.m.
NED1 Entity disambiguation (via context triple) batch_69cb5bb719a08190a0545a361f559bf7 completed March 31, 2026, 5:29 a.m.
Created at: March 30, 2026, 5:01 p.m.