Triple
T21428764
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ford GT40 |
E528627
|
entity |
| Predicate | notableDrivers |
P7128
|
FINISHED |
| Object | Chris Amon |
—
|
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: Chris Amon | Statement: [Ford GT40, notableDrivers, Chris Amon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Chris Amon Context triple: [Ford GT40, notableDrivers, Chris Amon]
-
A.
Chris Amon
chosen
Chris Amon was a highly talented New Zealand racing driver best known for his success in sports car racing and Formula One during the 1960s and 1970s.
-
B.
Daniel Gurney
Daniel Gurney was a 19th-century English banker, antiquary, and genealogist from the prominent Gurney family of Norfolk.
-
C.
Denny Hulme
Denny Hulme was a New Zealand racing driver who won the 1967 Formula One World Championship and became known for his toughness and success in both F1 and sports car racing.
-
D.
Jim Clark
Jim Clark is an American entrepreneur and computer scientist best known for co-founding Netscape and Silicon Graphics, playing a pivotal role in the early commercial development of the internet and computer graphics.
-
E.
Jim Clark
Jim Clark was a British film editor renowned for his work on numerous acclaimed movies across several decades, including major Hollywood and British productions.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0c455f3688190810bc96365791b0f |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69ee813db52c8190ac933bc6ec4dbf77 |
completed | April 26, 2026, 9:18 p.m. |
Created at: April 16, 2026, 5:49 p.m.