Triple
T10826418
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ximian |
E255507
|
entity |
| Predicate | notableEmployee |
P304
|
FINISHED |
| Object | Nat Friedman |
E888288
|
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: Nat Friedman | Statement: [Ximian, notableEmployee, Nat Friedman]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nat Friedman Context triple: [Ximian, notableEmployee, Nat Friedman]
-
A.
Nat Friedman
chosen
Nat Friedman is an American entrepreneur and software developer best known as the co-founder of Ximian and former CEO of GitHub.
-
B.
Nir Friedman
Nir Friedman is a computer scientist and computational biologist known for his influential work on probabilistic graphical models and their applications to biological data.
-
C.
Jonathan Friedman
Jonathan Friedman is a relatively common personal name shared by multiple individuals across various professional fields, including academia, law, and the arts.
-
D.
Jeffrey Friedman
Jeffrey Friedman is an American molecular geneticist best known for co-discovering the hormone leptin and elucidating its role in regulating body weight and obesity.
-
E.
Jeffrey Friedman
Jeffrey Friedman is an American documentary filmmaker known for co-directing acclaimed non-fiction films, often exploring cultural and social issues.
- 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_69d6aa8081448190a9324184f2bd1c26 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d734d1c24881909f56d56207cccbef |
completed | April 9, 2026, 5:10 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69deb1096cbc81908f3eda562c2da042 |
completed | April 14, 2026, 9:26 p.m. |
Created at: April 8, 2026, 9:19 p.m.