Triple
T2473188
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Miller Brewing Company |
E55022
|
entity |
| Predicate | hasKeyBrand |
P11989
|
FINISHED |
| Object | Miller 64 |
E270796
|
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: Miller 64 | Statement: [Miller Brewing Company, hasKeyBrand, Miller 64]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Miller 64 Context triple: [Miller Brewing Company, hasKeyBrand, Miller 64]
-
A.
Miller 64
chosen
Miller 64 is a light, low-calorie American lager beer marketed as a lower-carb option for calorie-conscious drinkers.
-
B.
Sauer
Sauer is a German surname borne by various notable individuals in fields such as science, politics, and the arts.
-
C.
Martz
Martz is a surname most notably associated with Mike Martz, an American football coach known for his innovative offensive strategies in the NFL.
-
D.
Alvarez
Alvarez is a common Spanish surname borne by numerous notable figures across fields such as science, sports, and the arts.
-
E.
Yukon Striker
Yukon Striker is a record-breaking dive roller coaster at Canada's Wonderland known for its 90-degree drop, underwater tunnel, and high speeds.
- 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_69ab49e279e88190ab10d7248aea9d11 |
completed | March 6, 2026, 9:40 p.m. |
| NER | Named-entity recognition | batch_69abd149c10c819098ff75f176972c8b |
completed | March 7, 2026, 7:18 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69af2b7af0d4819080fda496670bc5d7 |
completed | March 9, 2026, 8:20 p.m. |
Created at: March 6, 2026, 9:45 p.m.