Triple

T18705582
Position Surface form Disambiguated ID Type / Status
Subject Apache Parquet E457357 entity
Predicate compressionCodec P27672 FINISHED
Object Snappy
Snappy is a fast, lightweight compression algorithm developed by Google, optimized for high-speed data compression and decompression rather than maximum compression ratio.
 E1339013 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: Snappy | Statement: [Apache Parquet, compressionCodec, Snappy]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Snappy
Context triple: [Apache Parquet, compressionCodec, Snappy]
  • A. Snappy
    Snappy is Canonical Ltd.'s software packaging and deployment system designed for secure, containerized applications across Linux-based platforms.
  • B. Sharp
    Sharp is a common English surname borne by numerous notable individuals across politics, sports, academia, and the arts.
  • C. Sharp
    Sharp is a Japanese electronics manufacturer best known for producing consumer devices such as mobile phones, televisions, and display technologies.
  • D. Snub
    Snub is the nickname of Snub Pollard, an Australian-born silent film comedian known for his work in early Hollywood slapstick comedies.
  • E. Quick
    Quick is a character associated with the boxer and entertainer Sugar Ray, likely appearing in media or promotional contexts connected to his persona.
  • 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: Snappy
Triple: [Apache Parquet, compressionCodec, Snappy]
Generated description
Snappy is a fast, lightweight compression algorithm developed by Google, optimized for high-speed data compression and decompression rather than maximum compression ratio.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Snappy
Target entity description: Snappy is a fast, lightweight compression algorithm developed by Google, optimized for high-speed data compression and decompression rather than maximum compression ratio.
  • A. Snappy
    Snappy is Canonical Ltd.'s software packaging and deployment system designed for secure, containerized applications across Linux-based platforms.
  • B. Sharp
    Sharp is a common English surname borne by numerous notable individuals across politics, sports, academia, and the arts.
  • C. Sharp
    Sharp is a Japanese electronics manufacturer best known for producing consumer devices such as mobile phones, televisions, and display technologies.
  • D. Snub
    Snub is the nickname of Snub Pollard, an Australian-born silent film comedian known for his work in early Hollywood slapstick comedies.
  • E. Quick
    Quick is a character associated with the boxer and entertainer Sugar Ray, likely appearing in media or promotional contexts connected to his persona.
  • 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_69d8d392aad081909fe31aa03e6e97d1 completed April 10, 2026, 10:40 a.m.
NER Named-entity recognition batch_69e5671717b88190974f542015f641e8 completed April 19, 2026, 11:36 p.m.
NED1 Entity disambiguation (via context triple) batch_6a052b3a95888190b09328d459164071 completed May 14, 2026, 1:54 a.m.
NEDg Description generation batch_6a052dec70cc8190b66909ce6225b2bb completed May 14, 2026, 2:05 a.m.
NED2 Entity disambiguation (via description) batch_6a052edba614819084d818fe1cc84ca4 completed May 14, 2026, 2:09 a.m.
Created at: April 10, 2026, 11:49 a.m.