Triple

T18158217
Position Surface form Disambiguated ID Type / Status
Subject SHA-2 E434687 entity
Predicate definedIn P775 FINISHED
Object Secure Hash Standard NE NERFINISHED

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: Secure Hash Standard | Statement: [SHA-2, definedIn, Secure Hash Standard]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Secure Hash Standard
Context triple: [SHA-2, definedIn, Secure Hash Standard]
  • A. Secure Hash Standard chosen
    The Secure Hash Standard is a U.S. federal standard that specifies secure hash algorithms (such as the SHA family) used for generating fixed-size cryptographic hashes to ensure data integrity and support digital signatures.
  • B. SHA-2
    SHA-2 is a family of cryptographic hash functions widely used for data integrity, digital signatures, and security protocols on the internet.
  • C. Message-Digest Algorithm 5
    Message-Digest Algorithm 5 (MD5) is a widely known but now cryptographically broken hash function that produces a 128-bit hash value and was once commonly used for checksums and data integrity verification.
  • D. SHA-256
    SHA-256 is a widely used cryptographic hash function from the SHA-2 family that produces a 256-bit hash value for securing data integrity and authentication.
  • E. Merkle–Damgård construction
    The Merkle–Damgård construction is a fundamental method for building collision-resistant cryptographic hash functions from fixed-size compression functions, used in many classic hash algorithms like MD5 and SHA-1.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8b90b7a188190b3fc7b8d4a6cd20a completed April 10, 2026, 8:47 a.m.
NER Named-entity recognition batch_69e4dec02d9c81909ac6203b7d59c405 completed April 19, 2026, 1:55 p.m.
Created at: April 10, 2026, 10:30 a.m.