Triple
T7894068
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Power Fx |
E183304
|
entity |
| Predicate | usedIn |
P98
|
FINISHED |
| Object | Power Virtual Agents |
E188106
|
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: Power Virtual Agents | Statement: [Power Fx, usedIn, Power Virtual Agents]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Power Virtual Agents Context triple: [Power Fx, usedIn, Power Virtual Agents]
-
A.
Power Virtual Agents
chosen
Power Virtual Agents is a Microsoft low-code platform service for building and deploying AI-powered chatbots that can interact with users across websites, apps, and messaging channels.
-
B.
Dialogflow
Dialogflow is a Google Cloud service for building conversational interfaces, such as chatbots and voice apps, that understand natural language.
-
C.
ChatGPT Enterprise
ChatGPT Enterprise is OpenAI’s business-grade version of ChatGPT, offering enhanced security, admin controls, and scalable access to advanced AI capabilities for organizations.
-
D.
UiPath
UiPath is a leading global software company specializing in robotic process automation (RPA) platforms that help organizations automate repetitive digital tasks.
-
E.
Vertex AI
Vertex AI is Google Cloud’s unified machine learning platform for building, training, and deploying ML models at scale.
- 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_69ca828c474c8190a254d6499871eaff |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb3a15a7e88190a05474844817e5d2 |
completed | March 31, 2026, 3:05 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cb5ba9c2ac8190b8faf1518390dff4 |
completed | March 31, 2026, 5:29 a.m. |
Created at: March 30, 2026, 5:01 p.m.