Triple
T31933906
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hárslevelű |
E815331
|
entity |
| Predicate | typicalUseInTokaj |
P206118
|
FINISHED |
| Object | blended with Furmint |
E815330
|
NE 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: blended with Furmint | Statement: [Hárslevelű, typicalUseInTokaj, blended with Furmint]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalUseInTokaj Context triple: [Hárslevelű, typicalUseInTokaj, blended with Furmint]
-
A.
typicalGrapeUse
Indicates how grapes are most commonly or characteristically used or purposed (e.g., for eating fresh, wine-making, juice, or raisins).
-
B.
typicalUseInChampagne
Indicates that something is commonly or characteristically used in the production, serving, or enjoyment of champagne.
-
C.
primaryGrapeUse
Indicates that a grape variety is primarily used for a particular purpose, such as winemaking, table consumption, or raisin production.
-
D.
wineProfileUse
Indicates how a wine is intended to be used or enjoyed, such as its typical serving context or pairing purpose.
-
E.
usesWineType
Indicates that one entity makes use of, incorporates, or is associated with a particular type or category of wine.
- F. None of above. chosen
Provenance (5 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_69f348f3035c81908558e2339955abb3 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_6a037c9141dc819098d7fcc36e69882c |
completed | May 12, 2026, 7:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a2e8a3f56a08190b5fe26e362aa0d03 |
completed | June 14, 2026, 11:02 a.m. |
| PD | Predicate disambiguation | batch_6a0379eaa540819095a1c5d9f3513f9b |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037c7fb9f88190b384b1b68200aef0 |
completed | May 12, 2026, 7:16 p.m. |
Created at: May 1, 2026, 12:04 a.m.