Triple

T6408947
Position Surface form Disambiguated ID Type / Status
Subject NetBSD E127657 entity
Predicate supportsFileSystem P203 FINISHED
Object FFSv2 E198692 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: FFSv2 | Statement: [NetBSD, supportsFileSystem, FFSv2]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: FFSv2
Context triple: [NetBSD, supportsFileSystem, FFSv2]
  • A. FFSv2 chosen
    FFSv2 is the second-generation Fast File System used by NetBSD and other BSD-derived operating systems, offering improved performance, larger file and filesystem size support, and enhanced reliability over the original FFS.
  • B. Amazon FSx
    Amazon FSx is a fully managed file storage service from AWS that provides high-performance, scalable, and feature-rich file systems built on popular file system technologies.
  • C. FFS
    FFS is the commonly used abbreviation for the Swiss Federal Railways, the national railway company of Switzerland.
  • D. F2FS
    F2FS (Flash-Friendly File System) is a Linux file system optimized for NAND flash-based storage devices, designed to improve performance and lifespan on solid-state media.
  • E. 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.
  • 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_69c0083723d88190b1e37b19df162c08 completed March 22, 2026, 3:18 p.m.
NER Named-entity recognition batch_69c068cdf25881908d42a5d979637ad6 completed March 22, 2026, 10:10 p.m.
NED1 Entity disambiguation (via context triple) batch_69c638b38c888190aa2433173db64c90 completed March 27, 2026, 7:58 a.m.
Created at: March 22, 2026, 4:41 p.m.