Triple

T2792608
Position Surface form Disambiguated ID Type / Status
Subject GNU Tar E61962 entity
Predicate supportsCompression P203 FINISHED
Object bzip2
bzip2 is a free and open-source data compression program known for its high compression ratios using the Burrows–Wheeler algorithm.
 E299198 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: bzip2 | Statement: [GNU Tar, supportsCompression, bzip2]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: bzip2
Context triple: [GNU Tar, supportsCompression, bzip2]
  • A. Zip2
    Zip2 was an early online city guide and business directory software company from the late 1990s that provided web-based publishing tools for newspapers.
  • B. UPX
    UPX is an executable packer and compressor commonly used to reduce the size of binary programs.
  • C. BZ
    BZ is the two-letter ISO 3166-1 alpha-2 country code assigned to Belize.
  • D. BZ
    BZ is the commonly used abbreviation for the Ministry of Foreign Affairs of the Netherlands, which is responsible for the country’s foreign policy and international relations.
  • E. LZA
    LZA is the regional vehicle registration code assigned to motor vehicles registered in the city of Zamość in Poland.
  • 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: bzip2
Triple: [GNU Tar, supportsCompression, bzip2]
Generated description
bzip2 is a free and open-source data compression program known for its high compression ratios using the Burrows–Wheeler algorithm.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: bzip2
Target entity description: bzip2 is a free and open-source data compression program known for its high compression ratios using the Burrows–Wheeler algorithm.
  • A. Zip2
    Zip2 was an early online city guide and business directory software company from the late 1990s that provided web-based publishing tools for newspapers.
  • B. UPX
    UPX is an executable packer and compressor commonly used to reduce the size of binary programs.
  • C. BZ
    BZ is the two-letter ISO 3166-1 alpha-2 country code assigned to Belize.
  • D. BZ
    BZ is the commonly used abbreviation for the Ministry of Foreign Affairs of the Netherlands, which is responsible for the country’s foreign policy and international relations.
  • E. LZA
    LZA is the regional vehicle registration code assigned to motor vehicles registered in the city of Zamość in Poland.
  • 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_69ab4b7f51d881908768300ebd2fbdae completed March 6, 2026, 9:47 p.m.
NER Named-entity recognition batch_69abe0895e5881909702e69aaee5c425 completed March 7, 2026, 8:23 a.m.
NED1 Entity disambiguation (via context triple) batch_69afc65ebe788190859012e930918b05 completed March 10, 2026, 7:21 a.m.
NEDg Description generation batch_69afc6c6c620819098b76db174a6f98e completed March 10, 2026, 7:22 a.m.
NED2 Entity disambiguation (via description) batch_69afc72b2e3c8190aad78ac8924f07af completed March 10, 2026, 7:24 a.m.
Created at: March 6, 2026, 9:58 p.m.