Triple
T7717065
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jyoti Bansal |
E174911
|
entity |
| Predicate | founded |
P104
|
FINISHED |
| Object | AppDynamics |
E33851
|
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: AppDynamics | Statement: [Jyoti Bansal, founded, AppDynamics]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: AppDynamics Context triple: [Jyoti Bansal, founded, AppDynamics]
-
A.
AppDynamics
chosen
AppDynamics is an application performance monitoring and observability company that provides tools to track, analyze, and optimize the performance of software applications and IT infrastructure.
-
B.
New Relic
New Relic is a software analytics and application performance monitoring company that provides tools for tracking and optimizing the performance of web and mobile applications.
-
C.
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.
-
D.
Appirio
Appirio is a cloud services and consulting company known for helping enterprises implement and optimize platforms like Salesforce and Workday.
-
E.
Sumo Logic
Sumo Logic is a cloud-native machine data analytics and log management platform that helps organizations monitor, troubleshoot, and secure their applications and infrastructure in real time.
- 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_69c6995c463c8190a14458036249d419 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c702cd0ddc8190aa23d998f55d0bd6 |
completed | March 27, 2026, 10:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8b50cf3208190af9bb2d4126d381b |
completed | March 29, 2026, 5:13 a.m. |
Created at: March 27, 2026, 4:05 p.m.