Triple
T21439281
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Coburg bratwurst |
E528894
|
entity |
| Predicate | meatContent |
P143948
|
FINISHED |
| Object | atLeast85Percent |
—
|
LITERAL 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: atLeast85Percent | Statement: [Coburg bratwurst, meatContent, atLeast85Percent]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: meatContent Context triple: [Coburg bratwurst, meatContent, atLeast85Percent]
-
A.
meatType
Indicates the specific category or kind of meat associated with an entity.
-
B.
typicalMeat
Indicates that something is commonly or characteristically used or regarded as meat in a given context.
-
C.
meatQuality
Indicates the assessed level or characteristics of quality associated with a given piece or type of meat.
-
D.
notableMeatProduct
Indicates that one entity is a meat-based product that is especially prominent, well-known, or significant in relation to the other entity.
-
E.
meatPreparation
Indicates the method or process by which meat is treated, cooked, or otherwise prepared for consumption.
- F. None of above. chosen
Provenance (4 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_69e0c4569fa081908101baa24f8745db |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69e8b6feb2e48190ba5649f16a8bbbda |
completed | April 22, 2026, 11:54 a.m. |
| PD | Predicate disambiguation | batch_69e61639ee288190889ffd500d1260f6 |
completed | April 20, 2026, 12:04 p.m. |
| PDg | Predicate description generation | batch_69e6190163448190a2404b396215c686 |
completed | April 20, 2026, 12:16 p.m. |
Created at: April 16, 2026, 6:04 p.m.