Triple
T19434464
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Oracle AI Services |
E486194
|
entity |
| Predicate | includesService |
P1393
|
FINISHED |
| Object | OCI Anomaly Detection |
—
|
NE NERFINISHED |
How this triple was built (3 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: OCI Anomaly Detection | Statement: [Oracle AI Services, includesService, OCI Anomaly Detection]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: OCI Anomaly Detection Context triple: [Oracle AI Services, includesService, OCI Anomaly Detection]
-
A.
Anomaly Detector
Anomaly Detector is an Azure Cognitive Services offering that uses machine learning to automatically detect unusual patterns and outliers in time-series or other data.
-
B.
Oracle Cloud Guard
Oracle Cloud Guard is a security and compliance monitoring service for Oracle Cloud Infrastructure that continuously detects misconfigurations and threats and helps automate remediation.
-
C.
Outlier Analysis
Outlier Analysis is a comprehensive book by Charu C. Aggarwal that systematically covers the theory, algorithms, and applications of detecting anomalous data in various domains.
-
D.
Oracle Machine Learning
Oracle Machine Learning is a suite of in-database machine learning algorithms and tools from Oracle that enables data scientists and analysts to build, deploy, and manage predictive models directly within Oracle databases.
-
E.
LOF outlier detection algorithm
The LOF (Local Outlier Factor) outlier detection algorithm is an unsupervised data mining method that identifies anomalous data points by comparing their local density to that of their neighbors.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: OCI Anomaly Detection Target entity description: OCI Anomaly Detection is an Oracle Cloud Infrastructure AI service that uses machine learning to automatically detect unusual patterns and anomalies in time-series and other operational data.
-
A.
Anomaly Detector
Anomaly Detector is an Azure Cognitive Services offering that uses machine learning to automatically detect unusual patterns and outliers in time-series or other data.
-
B.
Oracle Cloud Guard
Oracle Cloud Guard is a security and compliance monitoring service for Oracle Cloud Infrastructure that continuously detects misconfigurations and threats and helps automate remediation.
-
C.
Outlier Analysis
Outlier Analysis is a comprehensive book by Charu C. Aggarwal that systematically covers the theory, algorithms, and applications of detecting anomalous data in various domains.
-
D.
Oracle Machine Learning
Oracle Machine Learning is a suite of in-database machine learning algorithms and tools from Oracle that enables data scientists and analysts to build, deploy, and manage predictive models directly within Oracle databases.
-
E.
LOF outlier detection algorithm
The LOF (Local Outlier Factor) outlier detection algorithm is an unsupervised data mining method that identifies anomalous data points by comparing their local density to that of their neighbors.
- F. None of above. chosen
Provenance (2 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_69d8e8d7ad488190a3373045029b0f3b |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e6335e8f7881909c8b28886521046e |
completed | April 20, 2026, 2:08 p.m. |
Created at: April 10, 2026, 1:37 p.m.