Triple
T8969419
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bill Maris |
E214224
|
entity |
| Predicate | notableInvestment |
P3488
|
FINISHED |
| Object | Cloudera |
E387790
|
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: Cloudera | Statement: [Bill Maris, notableInvestment, Cloudera]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cloudera Context triple: [Bill Maris, notableInvestment, Cloudera]
-
A.
Cloudera
chosen
Cloudera is an enterprise data management and analytics company best known for its platform built on Apache Hadoop and related open-source big data technologies.
-
B.
CDH
CDH is the College of Humanities at EPFL, responsible for teaching and research in human and social sciences within the Swiss engineering and technology university.
-
C.
Hadoop
Hadoop is an open-source framework that enables distributed storage and parallel processing of large data sets across clusters of commodity hardware.
-
D.
Databricks
Databricks is a cloud-based data and AI company best known for its unified analytics platform built around Apache Spark, enabling large-scale data engineering, data science, and machine learning workloads.
-
E.
Apache Hive
Apache Hive is a data warehouse and SQL-like query system built on top of Hadoop for managing and analyzing large datasets stored in distributed storage.
- 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_69ca839dbf608190a2f5990477115d29 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc6765babc8190a4a3b79aa21047c8 |
completed | April 1, 2026, 12:31 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cfc96006e48190978e4ccdedc48b41 |
completed | April 3, 2026, 2:06 p.m. |
Created at: March 30, 2026, 7:01 p.m.