Triple
T12676727
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tsolikouri |
E302831
|
entity |
| Predicate | wineAcidityLevel |
P2070
|
FINISHED |
| Object | medium to 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: medium to high | Statement: [Tsolikouri, wineAcidityLevel, medium to high]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: wineAcidityLevel Context triple: [Tsolikouri, wineAcidityLevel, medium to high]
-
A.
wineAcidityType
Indicates the type or category of acidity associated with a given wine.
-
B.
acidityLevel
chosen
Indicates the degree or intensity of acidity associated with an entity, typically measured by pH or a comparable scale.
-
C.
wineQualityLevelProduced
Indicates the quality level or grade assigned to the wine that is produced in the described production event or process.
-
D.
tanninLevel
Indicates the degree or intensity of tannins present in or associated with something, typically a beverage like wine or tea.
-
E.
hasBitternessLevel
Indicates that an entity is associated with a specific degree or intensity of bitterness.
- 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_69d7bdee64a08190801c6d470aefd723 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d961b0d9c88190a05d6cbcb7a1642d |
completed | April 10, 2026, 8:46 p.m. |
| PD | Predicate disambiguation | batch_69d960bb64ec8190bd0400cf0cc8b0a7 |
completed | April 10, 2026, 8:42 p.m. |
Created at: April 9, 2026, 5:20 p.m.