Triple

T3244521
Position Surface form Disambiguated ID Type / Status
Subject Solaris E68039 entity
Predicate feature P374 FINISHED
Object ZFS compression E68042 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: ZFS compression | Statement: [Solaris, feature, ZFS compression]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: ZFS compression
Context triple: [Solaris, feature, ZFS compression]
  • A. ZFS file system chosen
    The ZFS file system is a combined file system and logical volume manager known for its advanced features like data integrity verification, snapshots, and efficient storage management, originally created for enterprise-grade reliability and scalability.
  • B. gzip
    gzip is a widely used GNU file compression utility that reduces file size using the DEFLATE algorithm, commonly producing .gz archives on Unix-like systems.
  • C. Btrfs
    Btrfs is a modern copy-on-write Linux file system designed for advanced features like snapshots, checksumming, and efficient storage management.
  • D. bzip2
    bzip2 is a free and open-source data compression program known for its high compression ratios using the Burrows–Wheeler algorithm.
  • E. Compressor
    Compressor is Apple's professional video and audio encoding and transcoding application used to create optimized media outputs for various formats and platforms.
  • 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_69ad858e4c708190aa31d486cfee8a6a completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69adaf1982448190b3d60c9e4471421f completed March 8, 2026, 5:17 p.m.
NED1 Entity disambiguation (via context triple) batch_69b2775be7c88190ba60b191f1c51e19 completed March 12, 2026, 8:20 a.m.
Created at: March 8, 2026, 3:08 p.m.