Triple
T14764474
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sanjay Ghemawat |
E346957
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | MapReduce: Simplified Data Processing on Large Clusters |
E185673
|
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: MapReduce: Simplified Data Processing on Large Clusters | Statement: [Sanjay Ghemawat, notableWork, MapReduce: Simplified Data Processing on Large Clusters]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: MapReduce: Simplified Data Processing on Large Clusters Context triple: [Sanjay Ghemawat, notableWork, MapReduce: Simplified Data Processing on Large Clusters]
-
A.
MapReduce
chosen
MapReduce is a programming model and processing framework for distributed computation of large data sets across clusters of computers.
-
B.
Google MapReduce
Google MapReduce is a programming model and processing framework developed by Google for large-scale distributed data processing 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.
Hadoop
Hadoop is an open-source framework that enables distributed storage and parallel processing of large data sets across clusters of commodity hardware.
-
E.
Mining of Massive Datasets
"Mining of Massive Datasets" is a widely used textbook that introduces practical and scalable data mining and machine learning techniques for analyzing large-scale datasets.
- 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_69d822e8896c819091169882f9b20486 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69dec7f3a1608190b1b17624003a0c7f |
completed | April 14, 2026, 11:04 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe0cf4cef081909fa62125f43b36bc |
completed | May 8, 2026, 4:19 p.m. |
Created at: April 10, 2026, 1:30 a.m.