Triple
T5433064
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Siebel Systems |
E121541
|
entity |
| Predicate | notableProduct |
P1448
|
FINISHED |
| Object | Siebel CRM |
E21926
|
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: Siebel CRM | Statement: [Siebel Systems, notableProduct, Siebel CRM]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Siebel CRM Context triple: [Siebel Systems, notableProduct, Siebel CRM]
-
A.
Siebel
Siebel is a surname most prominently associated with Jennifer Siebel Newsom, an American documentary filmmaker and the First Partner of California.
-
B.
Siebel Systems
chosen
Siebel Systems was a leading enterprise software company best known for pioneering customer relationship management (CRM) solutions for large organizations.
-
C.
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.
-
D.
PeopleSoft
PeopleSoft is an enterprise software company best known for its human resources and financial management applications, later integrated into Oracle’s product portfolio.
-
E.
Appirio
Appirio is a cloud services and consulting company known for helping enterprises implement and optimize platforms like Salesforce and Workday.
- 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_69bd463c65f0819082ee6483ab4b466a |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd91ae18cc8190aefe610f91b5382c |
completed | March 20, 2026, 6:27 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf88e4ebe48190bf1d8643149a88f0 |
completed | March 22, 2026, 6:15 a.m. |
Created at: March 20, 2026, 2:06 p.m.