Triple
T5102302
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rocket eBook |
E115008
|
entity |
| Predicate | coDevelopedBy |
P3324
|
FINISHED |
| Object | Marc Tarpenning |
E20931
|
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: Marc Tarpenning | Statement: [Rocket eBook, coDevelopedBy, Marc Tarpenning]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Marc Tarpenning Context triple: [Rocket eBook, coDevelopedBy, Marc Tarpenning]
-
A.
Marc Tarpenning
chosen
Marc Tarpenning is an American engineer and entrepreneur best known as a co-founder of electric vehicle and clean energy company Tesla, Inc.
-
B.
Joel McKinnon Miller
Joel McKinnon Miller is an American character actor best known for playing the affable Detective Norm Scully on the television comedy series "Brooklyn Nine-Nine."
-
C.
Brendan Hunt
Brendan Hunt is an American actor, writer, and comedian best known for co-creating and starring in the acclaimed television series "Ted Lasso."
-
D.
Tim Laudner
Tim Laudner is a former Major League Baseball catcher best known for his years with the Minnesota Twins, including their 1987 World Series championship team.
-
E.
Ethan Embry
Ethan Embry is an American actor known for his roles in 1990s films such as "Empire Records," "Can't Hardly Wait," and various television series.
- 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_69bd4440b3348190be1251fd8b7951f1 |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd7586a4a08190866aea6be625837c |
completed | March 20, 2026, 4:27 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69becfc467008190ae704139f21edae2 |
completed | March 21, 2026, 5:05 p.m. |
Created at: March 20, 2026, 1:41 p.m.