Triple
T12516296
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | bzip2 |
E299198
|
entity |
| Predicate | hasParallelImplementation |
P105401
|
FINISHED |
| Object | lbzip2 |
E986436
|
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: lbzip2 | Statement: [bzip2, hasParallelImplementation, lbzip2]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: lbzip2 Context triple: [bzip2, hasParallelImplementation, lbzip2]
-
A.
lbzip2
chosen
lbzip2 is a parallel, multi-threaded implementation of the bzip2 compression algorithm designed to provide faster compression and decompression on multi-core systems.
-
B.
pbzip2
pbzip2 is a parallel implementation of the bzip2 compression algorithm designed to take advantage of multi-core and multi-processor systems for faster file compression and decompression.
-
C.
bzip2
bzip2 is a free and open-source data compression program known for its high compression ratios using the Burrows–Wheeler algorithm.
-
D.
lzip
lzip is a lossless data compression program and file format known for its high compression ratios, data integrity features, and use in software distribution archives.
-
E.
LZIB
LZIB is the ICAO airport code for M. R. Štefánik Airport, the main international airport serving Bratislava, Slovakia.
- 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_69d6ada5cdd48190860d9ce30aff69be |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d960c2e5b88190a7cc16002b218d8a |
completed | April 10, 2026, 8:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f655745cec8190b5582eb4a339d501 |
completed | May 2, 2026, 7:50 p.m. |
Created at: April 8, 2026, 9:57 p.m.