Triple
T18799344
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | xarray |
E459722
|
entity |
| Predicate | supportsBackend |
P15794
|
FINISHED |
| Object | SciDB |
—
|
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: SciDB | Statement: [xarray, supportsBackend, SciDB]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: SciDB Context triple: [xarray, supportsBackend, SciDB]
-
A.
SciDB
chosen
SciDB is an open-source array database management system designed for large-scale scientific and multidimensional data analytics.
-
B.
H-Store
H-Store is a pioneering in-memory, distributed OLTP database system designed for high-throughput transaction processing on modern multicore hardware.
-
C.
Vertica
Vertica is a high-performance, column-oriented analytical database system designed for large-scale data warehousing and real-time analytics.
-
D.
ScyllaDB
ScyllaDB is a high-performance, distributed NoSQL database designed as a drop-in replacement for Apache Cassandra, optimized for low latency and high throughput.
-
E.
VoltDB
VoltDB is a high-performance, in-memory, distributed SQL database designed for real-time analytics and transaction processing at massive scale.
- 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_69d8d398c7d4819091cb2f7e48948aeb |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e5a02273b481909bc250144a0ace32 |
completed | April 20, 2026, 3:40 a.m. |
Created at: April 10, 2026, 11:53 a.m.