Triple
T25857252
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | chimarrão |
E651379
|
entity |
| Predicate | typicalYerbaCut |
P84047
|
FINISHED |
| Object | fine grind with powder |
—
|
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: fine grind with powder | Statement: [chimarrão, typicalYerbaCut, fine grind with powder]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalYerbaCut Context triple: [chimarrão, typicalYerbaCut, fine grind with powder]
-
A.
tipoDeCortes
Indicates a classification relationship where an entity is associated with one or more specific types of cuts.
-
B.
estimatedTeaWeight
Indicates the quantified amount of tea that is approximated or predicted in weight rather than precisely measured.
-
C.
teaType
Indicates the specific variety or category of tea associated with an entity.
-
D.
teaProduct
Indicates that something is a product related to tea, such as an item made from, flavored with, or intended for preparing or consuming tea.
-
E.
hasTypicalCut
chosen
Indicates that one entity is characterized by or associated with a standard or typical type of cut of another entity.
- 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_69e7ab39035c8190be15c8aaee1bb858 |
completed | April 21, 2026, 4:52 p.m. |
| NER | Named-entity recognition | batch_69fddac4e2f48190a9301d3422658b29 |
completed | May 8, 2026, 12:44 p.m. |
| PD | Predicate disambiguation | batch_69fdda06969c8190b5d033964ea2a690 |
completed | May 8, 2026, 12:41 p.m. |
Created at: April 22, 2026, 8:01 a.m.