Triple
T4278454
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Einstein AI |
E97095
|
entity |
| Predicate | integratesWith |
P1075
|
FINISHED |
| Object | MuleSoft |
E97094
|
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: MuleSoft | Statement: [Einstein AI, integratesWith, MuleSoft]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: MuleSoft Context triple: [Einstein AI, integratesWith, MuleSoft]
-
A.
MuleSoft
chosen
MuleSoft is an integration and API management platform that helps organizations connect applications, data, and devices across cloud and on-premises environments.
-
B.
Salesforce
Salesforce is a leading cloud-based customer relationship management (CRM) company known for its suite of enterprise applications for sales, service, marketing, and analytics.
-
C.
Appirio
Appirio is a cloud services and consulting company known for helping enterprises implement and optimize platforms like Salesforce and Workday.
-
D.
Gainsight
Gainsight is a customer success and product experience software company known for helping businesses reduce churn, drive expansion, and improve customer retention through data-driven insights and workflows.
-
E.
Palantir Technologies
Palantir Technologies is an American software company specializing in big data analytics platforms used by governments and large enterprises for intelligence, security, and operational decision-making.
- 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_69b34544be3c819084d1ab82d29f90c5 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b350201ac88190b9d8980da5f0d03d |
completed | March 12, 2026, 11:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5b7b708b481908c1683741f84ee55 |
completed | March 14, 2026, 7:32 p.m. |
Created at: March 12, 2026, 11:07 p.m.