Triple
T16702400
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jamie Zawinski |
E405884
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Jamie Zawinski |
E405884
|
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: Jamie Zawinski | Statement: [Jamie Zawinski, name, Jamie Zawinski]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jamie Zawinski Context triple: [Jamie Zawinski, name, Jamie Zawinski]
-
A.
Jamie Zawinski
chosen
Jamie Zawinski is an American programmer and early Netscape and Mozilla developer known for his influential role in the early web browser wars and open-source software movement.
-
B.
Phil Zimmermann
Phil Zimmermann is an American cryptographer best known as the creator of Pretty Good Privacy (PGP), a widely used email encryption software that helped popularize strong cryptography for the public.
-
C.
Jonathan Schwartz
Jonathan Schwartz is a film producer best known for his work on acclaimed independent movies such as "Like Crazy."
-
D.
Dan Wyllie
Dan Wyllie is an Australian actor known for his work in film, television, and theatre, including prominent roles in acclaimed Australian dramas.
-
E.
L. Peter Deutsch
L. Peter Deutsch is a computer scientist and software developer best known for creating the Ghostscript interpreter for the PostScript language and PDF files.
- 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_69d8838db21081909589220fd71440a4 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e383326d7081909ef4c3b724876513 |
completed | April 18, 2026, 1:12 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0091a0dee08190a67ed5df2008c91e |
completed | May 10, 2026, 2:09 p.m. |
Created at: April 10, 2026, 5:19 a.m.