Triple
T7939782
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Istio |
E184360
|
entity |
| Predicate | integratesWith |
P1075
|
FINISHED |
| Object | Grafana |
E699843
|
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: Grafana | Statement: [Istio, integratesWith, Grafana]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Grafana Context triple: [Istio, integratesWith, Grafana]
-
A.
Grafana
chosen
Grafana is an open-source analytics and visualization platform used to create interactive dashboards and monitor metrics from various data sources.
-
B.
Datadog
Datadog is a cloud-based monitoring and security platform that provides observability into applications, infrastructure, logs, and metrics for modern DevOps and IT teams.
-
C.
Looker
Looker is a modern business intelligence and data analytics platform that enables organizations to explore, visualize, and share insights from their data.
-
D.
Plotly
Plotly is an interactive, open-source graphing and data visualization library widely used in Python for creating rich, web-based charts and dashboards.
-
E.
Tableau
Tableau is a widely used data visualization and business intelligence software platform that enables users to analyze, explore, and present data through interactive dashboards and reports.
- 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_69ca8290c21c8190906a5ca6fe2b03c4 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb3b0983388190a77e8d5d899c5130 |
completed | March 31, 2026, 3:10 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cbe019a094819082baecdcb007c84f |
completed | March 31, 2026, 2:54 p.m. |
Created at: March 30, 2026, 5:08 p.m.