Triple
T5004167
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Côte-Rôtie |
E112445
|
entity |
| Predicate | grapeTrainingSystem |
P43323
|
FINISHED |
| Object | échalas (single stake) training |
—
|
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: échalas (single stake) training | Statement: [Côte-Rôtie, grapeTrainingSystem, échalas (single stake) training]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: grapeTrainingSystem Context triple: [Côte-Rôtie, grapeTrainingSystem, échalas (single stake) training]
-
A.
typicalVineTraining
chosen
Indicates that one entity is the standard or commonly used method of training or shaping the growth of another entity, typically in a vine or climbing context.
-
B.
trainingSystem
Indicates a system or framework used to train, instruct, or develop skills or knowledge in a target entity.
-
C.
grapeSource
Indicates that one entity is the origin or provider of grapes used by another entity.
-
D.
usesGrapeType
Indicates that one entity employs or incorporates a specific type or variety of grape in its composition, production, or process.
-
E.
trainingUse
Indicates that something is used for training purposes, such as preparing, educating, or improving the skills or performance of an 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_69bd4433d0b08190877e83959ef40d81 |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd7472a1dc8190942f568a81fdd961 |
completed | March 20, 2026, 4:23 p.m. |
| PD | Predicate disambiguation | batch_69bd714aee2481908fb0dd5fa2daf3a1 |
completed | March 20, 2026, 4:09 p.m. |
Created at: March 20, 2026, 1:35 p.m.