Triple
T15989945
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sumo Logic |
E387795
|
entity |
| Predicate | integratesWith |
P1075
|
FINISHED |
| Object | Okta |
E292745
|
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: Okta | Statement: [Sumo Logic, integratesWith, Okta]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Okta Context triple: [Sumo Logic, integratesWith, Okta]
-
A.
Okta
chosen
Okta is a cloud-based identity and access management platform that provides secure single sign-on, authentication, and user management for applications and services.
-
B.
SailPoint Technologies
SailPoint Technologies is a cybersecurity company specializing in identity and access management solutions for enterprises.
-
C.
Duo Security
Duo Security is a cybersecurity company best known for its cloud-based multi-factor authentication and zero-trust access solutions.
-
D.
Zscaler
Zscaler is a cloud-based information security company known for providing secure access and zero-trust networking solutions to enterprises worldwide.
-
E.
Entrust Technologies
Entrust Technologies is a cybersecurity company known for developing encryption and data protection solutions, including contributions to symmetric-key block cipher design.
- 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_69d86daa562c81908aacc179c0fe8fb5 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e157829ec08190aa4a683e29a0148a |
completed | April 16, 2026, 9:41 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffc3d2369081909efa2d4addf0cf2d |
completed | May 9, 2026, 11:31 p.m. |
Created at: April 10, 2026, 4:54 a.m.