Triple

T1711858
Position Surface form Disambiguated ID Type / Status
Subject PKCS #1 E37199 entity
Predicate focusesOn P31 FINISHED
Object RSA algorithm E5909 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: RSA algorithm | Statement: [PKCS #1, focusesOn, RSA algorithm]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: RSA algorithm
Context triple: [PKCS #1, focusesOn, RSA algorithm]
  • A. RSA chosen
    RSA is a widely used public-key cryptographic algorithm that enables secure key exchange and digital signatures in many internet security protocols.
  • B. RSA Security
    RSA Security is a pioneering American cybersecurity company best known for its contributions to public-key cryptography and secure data encryption technologies.
  • C. ElGamal
    ElGamal is a public-key cryptosystem based on the discrete logarithm problem, widely used for secure encryption and digital signatures in various cryptographic protocols.
  • D. Diffie–Hellman key exchange
    Diffie–Hellman key exchange is a foundational cryptographic protocol that enables two parties to securely establish a shared secret over an insecure communication channel.
  • E. Blum–Blum–Shub pseudorandom number generator
    The Blum–Blum–Shub pseudorandom number generator is a cryptographically secure generator based on the hardness of factoring large composite numbers, widely studied in theoretical computer science and cryptography.
  • 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_69a8861912dc8190931af43b4b9158a7 completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69aa6315afdc81908409435bb47e8ee0 completed March 6, 2026, 5:16 a.m.
NED1 Entity disambiguation (via context triple) batch_69ada0d1882c81908e02e36ab28e7fdc completed March 8, 2026, 4:16 p.m.
Created at: March 4, 2026, 7:30 p.m.