Triple
T18705583
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Apache Parquet |
E457357
|
entity |
| Predicate | compressionCodec |
P27672
|
FINISHED |
| Object | Gzip |
—
|
NE NERFINISHED |
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: Gzip | Statement: [Apache Parquet, compressionCodec, Gzip]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gzip Context triple: [Apache Parquet, compressionCodec, Gzip]
-
A.
gzip
chosen
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.
-
B.
zlib
zlib is a widely used software library that provides lossless data compression using the DEFLATE algorithm.
-
C.
Zip
"Zip" is a witty, patter-style show tune from the Rodgers and Hart musical *Pal Joey*, known for its satirical take on intellectual pretension.
-
D.
Zstandard
Zstandard is a fast, modern lossless data compression algorithm developed by Facebook that offers high compression ratios with low CPU usage.
-
E.
bzip2
bzip2 is a free and open-source data compression program known for its high compression ratios using the Burrows–Wheeler algorithm.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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. |
Created at: April 10, 2026, 11:49 a.m.