Triple
T1015800
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Siebel Systems |
E21926
|
entity |
| Predicate | notableProduct |
P1448
|
FINISHED |
| Object | Siebel Marketing |
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 Marketing | Statement: [Siebel Systems, notableProduct, Siebel Marketing]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Siebel Marketing Context triple: [Siebel Systems, notableProduct, Siebel Marketing]
-
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.
Marketo
Marketo is a leading marketing automation software platform that helps businesses manage and optimize digital marketing campaigns and customer engagement.
-
E.
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.
- 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_69a493c68e24819080ed0ee8bcfd5ce0 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b7c1e9d08190baf7e81f3777168d |
completed | March 1, 2026, 10:03 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac5381e020819094e02c3d20acab95 |
completed | March 7, 2026, 4:34 p.m. |
Created at: March 1, 2026, 7:41 p.m.