Triple
T7939032
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | etcd |
E184346
|
entity |
| Predicate | relatedTo |
P37
|
FINISHED |
| Object | ZooKeeper |
E185678
|
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: ZooKeeper | Statement: [etcd, relatedTo, ZooKeeper]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: ZooKeeper Context triple: [etcd, relatedTo, ZooKeeper]
-
A.
Apache ZooKeeper
chosen
Apache ZooKeeper is a centralized service for maintaining configuration information, naming, and distributed synchronization in large-scale distributed systems.
-
B.
Apache Kafka
Apache Kafka is a distributed event streaming platform widely used for building real-time data pipelines and streaming applications.
-
C.
Apache Mesos
Apache Mesos is an open-source cluster manager that abstracts CPU, memory, storage, and other resources away from machines to enable efficient deployment and scaling of distributed applications and frameworks.
-
D.
Apache Storm
Apache Storm is a distributed real-time computation system designed for processing large streams of data with low latency and high fault tolerance.
-
E.
Hadoop
Hadoop is an open-source framework that enables distributed storage and parallel processing of large data sets across clusters of commodity hardware.
- 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_69ca8290c21c8190906a5ca6fe2b03c4 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb3af0a2048190838d1aeda59fda0b |
completed | March 31, 2026, 3:09 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cb5c0e868481908748d340244ea8ea |
completed | March 31, 2026, 5:30 a.m. |
Created at: March 30, 2026, 5:08 p.m.