Triple
T19803792
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bryan Nesbitt |
E475754
|
entity |
| Predicate | employer |
P7
|
FINISHED |
| Object | Buick |
—
|
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: Buick | Statement: [Bryan Nesbitt, employer, Buick]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Buick Context triple: [Bryan Nesbitt, employer, Buick]
-
A.
Buick
chosen
Buick is an American automobile marque known for producing upscale, comfort-oriented vehicles positioned between mainstream and luxury brands.
-
B.
Oldsmobile
Oldsmobile was a historic American automobile marque known for pioneering innovations like the mass-produced Curved Dash and the Rocket V8 engine.
-
C.
Cadillac
Cadillac is a small city in northern Michigan known for its surrounding lakes, outdoor recreation, and role as a regional tourism and service hub.
-
D.
Cadillac
Cadillac is a Montreal Metro station on the Green Line serving the Mercier–Hochelaga-Maisonneuve borough in Montreal, Quebec, Canada.
-
E.
Cadillac
Cadillac is a luxury automobile brand known for its premium vehicles and long-standing association with American upscale motoring.
- 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_69d8e51bc4208190a1c57d8c5d1b15e4 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e654266e18819085698aed8b0e2ba8 |
completed | April 20, 2026, 4:28 p.m. |
Created at: April 10, 2026, 1:49 p.m.