Triple
T7984821
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Apache Spark |
E185661
|
entity |
| Predicate | canRunOn |
P11903
|
FINISHED |
| Object | Apache Mesos |
E184357
|
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: Apache Mesos | Statement: [Apache Spark, canRunOn, Apache Mesos]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Apache Mesos Context triple: [Apache Spark, canRunOn, Apache Mesos]
-
A.
Apache Mesos
chosen
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.
-
B.
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.
-
C.
Apache ZooKeeper
Apache ZooKeeper is a centralized service for maintaining configuration information, naming, and distributed synchronization in large-scale distributed systems.
-
D.
Mesos agent
Mesos agent is the worker node process in an Apache Mesos cluster responsible for running and managing tasks and containers on individual machines.
-
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_69ca829a2cfc819083d591d58ec04075 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb3c4a55b881909a96133e56c0dffa |
completed | March 31, 2026, 3:15 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cbe0e0b2748190930c22c6157d1b07 |
completed | March 31, 2026, 2:57 p.m. |
Created at: March 30, 2026, 5:15 p.m.