Triple
T24893614
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Montlouis-sur-Loire AOC |
E623072
|
entity |
| Predicate | typicalSweetnessRange |
P62205
|
FINISHED |
| Object | from dry to lusciously sweet |
—
|
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: from dry to lusciously sweet | Statement: [Montlouis-sur-Loire AOC, typicalSweetnessRange, from dry to lusciously sweet]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalSweetnessRange Context triple: [Montlouis-sur-Loire AOC, typicalSweetnessRange, from dry to lusciously sweet]
-
A.
typicalSweetnessLevel
chosen
Indicates the usual or characteristic degree of sweetness associated with something.
-
B.
sweetenerType
Indicates the specific kind or category of sweetener associated with or used in relation to an entity.
-
C.
isSweet
Indicates that something possesses a sweet taste or quality.
-
D.
sweetnessOnset
Indicates the point in time or conditions under which sweetness first becomes perceptible in relation to something.
-
E.
sparklingWineSweetnessRange
Indicates the range of sweetness levels that a sparkling wine can have or is classified within.
- 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_69e2fac597708190a922bf39a49ec70a |
completed | April 18, 2026, 3:30 a.m. |
| NER | Named-entity recognition | batch_69f6db1f3ec48190a82e7d893d3c76ba |
completed | May 3, 2026, 5:20 a.m. |
| PD | Predicate disambiguation | batch_69f6d82adfa481908a5e196d2e18c73f |
completed | May 3, 2026, 5:07 a.m. |
Created at: April 18, 2026, 5:26 a.m.