Triple
T14502440
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pedro Ximénez sweet sherry |
E340175
|
entity |
| Predicate | residualSugarContent |
P112699
|
FINISHED |
| Object | very high |
—
|
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: very high | Statement: [Pedro Ximénez sweet sherry, residualSugarContent, very high]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: residualSugarContent Context triple: [Pedro Ximénez sweet sherry, residualSugarContent, very high]
-
A.
hasSugarContent
Indicates that one entity possesses or contains a specified amount or level of sugar.
-
B.
typicalSweetnessLevel
Indicates the usual or characteristic degree of sweetness associated with something.
-
C.
isSugarFree
Indicates that something does not contain sugar or has been formulated without added sugar.
-
D.
sugarContentCategory
chosen
Indicates the classification of something based on how much sugar it contains (e.g., low, medium, or high sugar content).
-
E.
hasSugarFreeVariant
Indicates that an item has a corresponding version or option that is formulated without sugar.
- 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_69d822d9c0408190b9a2b3643e58bb4d |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69de94e0f9048190a2d266cfa4f9dfb6 |
completed | April 14, 2026, 7:26 p.m. |
| PD | Predicate disambiguation | batch_69de5c4ccba08190a988bfda0bc9f5cb |
completed | April 14, 2026, 3:25 p.m. |
Created at: April 10, 2026, 1:21 a.m.