Triple
T12562466
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Michael Stonebraker |
E295382
|
entity |
| Predicate | knownFor |
P22
|
FINISHED |
| Object |
SciDB
SciDB is an open-source array database management system designed for large-scale scientific and multidimensional data analytics.
|
E991167
|
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: SciDB | Statement: [Michael Stonebraker, knownFor, SciDB]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: SciDB Context triple: [Michael Stonebraker, knownFor, SciDB]
-
A.
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.
-
B.
CockroachDB
CockroachDB is a distributed SQL database designed for horizontal scalability, strong consistency, and high fault tolerance across multiple nodes and regions.
-
C.
ColumnStore
ColumnStore is a columnar storage engine for MariaDB designed to support scalable, high-performance analytics and data warehousing workloads.
-
D.
RethinkDB
RethinkDB is an open-source, distributed NoSQL database designed for real-time applications by pushing live updates to clients as data changes.
-
E.
Greenplum
Greenplum is a massively parallel, open-source data warehouse and analytics platform designed for large-scale business intelligence and big data workloads.
- 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: SciDB Triple: [Michael Stonebraker, knownFor, SciDB]
Generated description
SciDB is an open-source array database management system designed for large-scale scientific and multidimensional data analytics.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: SciDB Target entity description: SciDB is an open-source array database management system designed for large-scale scientific and multidimensional data analytics.
-
A.
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.
-
B.
CockroachDB
CockroachDB is a distributed SQL database designed for horizontal scalability, strong consistency, and high fault tolerance across multiple nodes and regions.
-
C.
ColumnStore
ColumnStore is a columnar storage engine for MariaDB designed to support scalable, high-performance analytics and data warehousing workloads.
-
D.
RethinkDB
RethinkDB is an open-source, distributed NoSQL database designed for real-time applications by pushing live updates to clients as data changes.
-
E.
Greenplum
Greenplum is a massively parallel, open-source data warehouse and analytics platform designed for large-scale business intelligence and big data workloads.
- 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_69d6ad9cac2c81908e8a7bed82d1e21d |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d95494ae1c81908b9ee14b8ef92a65 |
completed | April 10, 2026, 7:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6558da7e0819086860bfaf394e2d8 |
completed | May 2, 2026, 7:50 p.m. |
| NEDg | Description generation | batch_69f65a0f1b2881908a7cb21c9de1a5c2 |
completed | May 2, 2026, 8:09 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f65ad4424c8190933759cca5d73c22 |
completed | May 2, 2026, 8:13 p.m. |
Created at: April 8, 2026, 11:48 p.m.