Triple
T19738726
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Myrna Fahey |
E474055
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Bronco |
—
|
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: Bronco | Statement: [Myrna Fahey, notableWork, Bronco]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bronco Context triple: [Myrna Fahey, notableWork, Bronco]
-
A.
Bronco
Bronco is a line of rugged sport-utility vehicles produced by Ford, known for their off-road capability and iconic boxy styling.
-
B.
Bronco
chosen
Bronco is a popular Mexican band known for its influential contributions to norteño and grupera music, blending romantic lyrics with danceable rhythms.
-
C.
Bisonte
Bisonte is the nickname of Italian former football striker Dario Hübner, renowned for his prolific goal-scoring in Serie A and Serie B.
-
D.
Bronk
Bronk is a surname most notably associated with Detlev W. Bronk, an influential American scientist and educator who helped shape modern biophysics and higher education policy.
-
E.
Bronko
Bronko is the nickname of Bronko Nagurski, a legendary National Football League fullback and professional wrestler known for his power and toughness in the 1930s.
- 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_69d8e517ebd48190979ee76723bcfadf |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e6515f6efc8190a3da113847464399 |
completed | April 20, 2026, 4:16 p.m. |
Created at: April 10, 2026, 1:47 p.m.