Triple
T5433060
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Siebel Systems |
E121541
|
entity |
| Predicate | foundedBy |
P104
|
FINISHED |
| Object | Thomas Siebel |
E121538
|
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: Thomas Siebel | Statement: [Siebel Systems, foundedBy, Thomas Siebel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Thomas Siebel Context triple: [Siebel Systems, foundedBy, Thomas Siebel]
-
A.
Thomas Siebel
chosen
Thomas Siebel is an American technology entrepreneur best known as the founder of Siebel Systems and later the cloud computing company C3.ai.
-
B.
Leonard Bosack
Leonard Bosack is an American computer engineer and entrepreneur best known as the co-founder of Cisco Systems, a pioneering company in computer networking and internet infrastructure.
-
C.
John Gage
John Gage is a name shared by several notable figures, including historical politicians, diplomats, and fictional characters in film and television.
-
D.
John Case
John Case is a relatively common personal name that may refer to multiple individuals across different fields, such as literature, academia, or public life.
-
E.
Jim Lewis
Jim Lewis is a writer best known for his extensive work on Muppet-related projects, contributing scripts and material for various films, shows, and attractions.
- 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_69bf3accb6748190989257c3b991a760 |
completed | March 22, 2026, 12:41 a.m. |
Created at: March 20, 2026, 2:06 p.m.