Triple

T8022300
Position Surface form Disambiguated ID Type / Status
Subject Encrypting File System E186766 entity
Predicate alsoKnownAs P39 FINISHED
Object EFS
EFS is a Windows feature that provides built-in file-level encryption to protect data stored on NTFS volumes.
 E707748 NE FINISHED

How this triple was built (4 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: EFS | Statement: [Encrypting File System, alsoKnownAs, EFS]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: EFS
Context triple: [Encrypting File System, alsoKnownAs, EFS]
  • A. ESFS
    ESFS is the acronym for the European System of Financial Supervision, the EU’s framework of supervisory authorities and bodies that oversee the stability and proper functioning of its financial system.
  • B. FSE
    FSE (Fast Software Encryption) is a leading international research conference focused on the design and analysis of symmetric-key cryptographic primitives and algorithms.
  • C. FSE
    FSE is the Faculty of Science and Engineering at the University of Groningen, encompassing a broad range of natural sciences, engineering, and technology disciplines.
  • D. FSE
    FSE is a premier international research conference on software engineering organized under ACM SIGSOFT.
  • E. XFS
    XFS is a high-performance 64-bit journaling file system originally developed by SGI, widely used on Linux for handling large files and parallel I/O workloads.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: EFS
Triple: [Encrypting File System, alsoKnownAs, EFS]
Generated description
EFS is a Windows feature that provides built-in file-level encryption to protect data stored on NTFS volumes.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: EFS
Target entity description: EFS is a Windows feature that provides built-in file-level encryption to protect data stored on NTFS volumes.
  • A. ESFS
    ESFS is the acronym for the European System of Financial Supervision, the EU’s framework of supervisory authorities and bodies that oversee the stability and proper functioning of its financial system.
  • B. FSE
    FSE (Fast Software Encryption) is a leading international research conference focused on the design and analysis of symmetric-key cryptographic primitives and algorithms.
  • C. FSE
    FSE is the Faculty of Science and Engineering at the University of Groningen, encompassing a broad range of natural sciences, engineering, and technology disciplines.
  • D. FSE
    FSE is a premier international research conference on software engineering organized under ACM SIGSOFT.
  • E. XFS
    XFS is a high-performance 64-bit journaling file system originally developed by SGI, widely used on Linux for handling large files and parallel I/O workloads.
  • F. None of above. chosen

Provenance (5 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_69ca82ac7fc081909b1398cf025423af completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cb3e8eab9c81908098e6b17957316c completed March 31, 2026, 3:25 a.m.
NED1 Entity disambiguation (via context triple) batch_69cc56cdce588190abba45c24dc7a5e7 completed March 31, 2026, 11:20 p.m.
NEDg Description generation batch_69cc58aac4288190a2be4691fc740171 completed March 31, 2026, 11:28 p.m.
NED2 Entity disambiguation (via description) batch_69cc5cbaefb481909eb325f0d27675c0 completed March 31, 2026, 11:46 p.m.
Created at: March 30, 2026, 5:21 p.m.