Triple
T7985453
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | YARN |
E185672
|
entity |
| Predicate | replaces |
P101
|
FINISHED |
| Object |
Hadoop MapReduce v1 JobTracker
Hadoop MapReduce v1 JobTracker was the central master service in early Hadoop versions responsible for scheduling, coordinating, and monitoring MapReduce jobs across a cluster.
|
E707878
|
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: Hadoop MapReduce v1 JobTracker | Statement: [YARN, replaces, Hadoop MapReduce v1 JobTracker]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hadoop MapReduce v1 JobTracker Context triple: [YARN, replaces, Hadoop MapReduce v1 JobTracker]
-
A.
MapReduce
MapReduce is a programming model and processing framework for distributed computation of large data sets across clusters of computers.
-
B.
Hadoop
Hadoop is an open-source framework that enables distributed storage and parallel processing of large data sets across clusters of commodity hardware.
-
C.
Apache Tez
Apache Tez is a distributed data processing framework designed for building high-performance batch and interactive data workflows on Hadoop.
-
D.
Google File System
Google File System is a distributed file system developed by Google to reliably store and process massive amounts of data across clusters of commodity hardware.
-
E.
ApplicationMaster
ApplicationMaster is the per-application coordinator in Hadoop YARN responsible for managing an application's lifecycle, resource requests, and task execution.
- 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: Hadoop MapReduce v1 JobTracker Triple: [YARN, replaces, Hadoop MapReduce v1 JobTracker]
Generated description
Hadoop MapReduce v1 JobTracker was the central master service in early Hadoop versions responsible for scheduling, coordinating, and monitoring MapReduce jobs across a cluster.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Hadoop MapReduce v1 JobTracker Target entity description: Hadoop MapReduce v1 JobTracker was the central master service in early Hadoop versions responsible for scheduling, coordinating, and monitoring MapReduce jobs across a cluster.
-
A.
MapReduce
MapReduce is a programming model and processing framework for distributed computation of large data sets across clusters of computers.
-
B.
Hadoop
Hadoop is an open-source framework that enables distributed storage and parallel processing of large data sets across clusters of commodity hardware.
-
C.
Apache Tez
Apache Tez is a distributed data processing framework designed for building high-performance batch and interactive data workflows on Hadoop.
-
D.
Google File System
Google File System is a distributed file system developed by Google to reliably store and process massive amounts of data across clusters of commodity hardware.
-
E.
ApplicationMaster
ApplicationMaster is the per-application coordinator in Hadoop YARN responsible for managing an application's lifecycle, resource requests, and task execution.
- 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_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_69cc567cfe548190bbd163a32c340bcc |
completed | March 31, 2026, 11:19 p.m. |
| NEDg | Description generation | batch_69cc58a8f3d08190bec84dc1ba3b5b84 |
completed | March 31, 2026, 11:28 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69cc5cb2963c81908a8dfbb1f84845bc |
completed | March 31, 2026, 11:45 p.m. |
Created at: March 30, 2026, 5:15 p.m.