Triple
T17710990
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Turkish tea |
E441563
|
entity |
| Predicate | hasBrewingMethod |
P53110
|
FINISHED |
| Object | concentrated tea in upper pot diluted with hot water from lower pot |
—
|
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: concentrated tea in upper pot diluted with hot water from lower pot | Statement: [Turkish tea, hasBrewingMethod, concentrated tea in upper pot diluted with hot water from lower pot]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasBrewingMethod Context triple: [Turkish tea, hasBrewingMethod, concentrated tea in upper pot diluted with hot water from lower pot]
-
A.
brewingMethod
chosen
Indicates the technique or process used to brew or prepare a beverage, typically coffee or tea.
-
B.
brewedAccordingTo
Indicates that something has been produced or prepared following a specified brewing method, recipe, or standard.
-
C.
coffeeProcessingMethods
Indicates the methods or techniques used to transform raw coffee cherries or beans into a consumable coffee product.
-
D.
teaType
Indicates the specific variety or category of tea associated with an entity.
-
E.
coffeeProfile
Indicates the characteristic flavor, aroma, and strength attributes that define a particular coffee.
- F. None of above.
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_69d8b9ea20b48190ace88bb46b01e6a9 |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e4729b5d3c819085613ed25dc6761d |
completed | April 19, 2026, 6:13 a.m. |
| PD | Predicate disambiguation | batch_69e3cde601d4819097903f471f1fe99a |
completed | April 18, 2026, 6:31 p.m. |
Created at: April 10, 2026, 10:05 a.m.