Triple
T9925948
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | HDFS |
E187921
|
entity |
| Predicate | fullName |
P16
|
FINISHED |
| Object | Hadoop Distributed File System |
E187921
|
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: Hadoop Distributed File System | Statement: [HDFS, fullName, Hadoop Distributed File System]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hadoop Distributed File System Context triple: [HDFS, fullName, Hadoop Distributed File System]
-
A.
Hadoop
Hadoop is an open-source framework that enables distributed storage and parallel processing of large data sets across clusters of commodity hardware.
-
B.
HDFS
chosen
HDFS (Hadoop Distributed File System) is a fault-tolerant, distributed file system designed to store and manage large volumes of data across clusters of commodity hardware.
-
C.
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.
-
D.
MapReduce
MapReduce is a programming model and processing framework for distributed computation of large data sets across clusters of computers.
-
E.
Apache HBase
Apache HBase is a distributed, scalable, NoSQL database designed for real-time read/write access to large datasets, typically running on top of the Hadoop ecosystem.
- 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_69ca82b22a688190b52c75bd48429c10 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cdb599e32c8190ac676fa89c131bb6 |
completed | April 2, 2026, 12:17 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d20e143660819097a9fa96365bc25a |
completed | April 5, 2026, 7:24 a.m. |
Created at: March 30, 2026, 8:43 p.m.