Triple
T10096272
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | president of Cruise Automation |
E215874
|
entity |
| Predicate | typicalSeniorityLevel |
P92411
|
FINISHED |
| Object | C-suite level |
—
|
LITERAL 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: C-suite level | Statement: [president of Cruise Automation, typicalSeniorityLevel, C-suite level]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalSeniorityLevel Context triple: [president of Cruise Automation, typicalSeniorityLevel, C-suite level]
-
A.
typicalExperienceLevel
Indicates the usual or most common level of experience associated with an entity in a given context.
-
B.
typeOfExperience
Indicates that one entity specifies the category or nature of an experience associated with another entity.
-
C.
seniorRank
Indicates that one entity holds a higher or more senior rank or position than another entity.
-
D.
typicalDegree
Indicates the usual or characteristic level, intensity, or extent to which something holds or applies in a given context.
-
E.
trainingLevel
Indicates the degree or stage of training or skill development that an entity has attained.
- F. None of above. chosen
Provenance (4 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_69ca83a4947c8190823a7495dc5d96ed |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cdd0798c248190af675e30e280daa8 |
completed | April 2, 2026, 2:12 a.m. |
| PD | Predicate disambiguation | batch_69cd4b9b853c8190a2af993ce9b21309 |
completed | April 1, 2026, 4:45 p.m. |
| PDg | Predicate description generation | batch_69cd5150ae98819086c4f822114b4e2c |
completed | April 1, 2026, 5:09 p.m. |
Created at: March 30, 2026, 9:02 p.m.