Triple
T33026981
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jason A. Donenfeld |
E845068
|
entity |
| Predicate | hasProgrammingLanguageExperience |
P169209
|
FINISHED |
| Object | C |
—
|
NE NERFINISHED |
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 | Statement: [Jason A. Donenfeld, hasProgrammingLanguageExperience, C]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasProgrammingLanguageExperience Context triple: [Jason A. Donenfeld, hasProgrammingLanguageExperience, C]
-
A.
hasLanguageOfProgramming
chosen
Indicates that an entity uses, is implemented in, or is otherwise associated with a particular programming language.
-
B.
hasProgrammingDomain
Indicates that an entity is associated with or specializes in a particular programming domain or area of software development.
-
C.
programmingLanguageWorkedOn
Indicates that an entity has contributed work to the development or implementation of a particular programming language.
-
D.
hasProgrammingScope
Indicates that something (such as a role, task, or activity) involves or includes programming-related responsibilities or subject matter.
-
E.
programmedLanguageSkills
Indicates that an entity has been programmed or configured with the ability to understand or use specific languages.
- F. None of above.
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_69f34950749c8190ae05cd27adb16d58 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_6a0144c75aec81908bdd9e88f2af737a |
completed | May 11, 2026, 2:53 a.m. |
| PD | Predicate disambiguation | batch_6a014405a2d48190a1d58b0b907ad696 |
completed | May 11, 2026, 2:50 a.m. |
Created at: May 1, 2026, 1:23 a.m.