Triple
T19341802
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ribolla Gialla |
E483771
|
entity |
| Predicate | wineMarketSegment |
P135482
|
FINISHED |
| Object | niche |
—
|
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: niche | Statement: [Ribolla Gialla, wineMarketSegment, niche]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: wineMarketSegment Context triple: [Ribolla Gialla, wineMarketSegment, niche]
-
A.
wineMarket
Indicates a commercial context or marketplace where wine is bought, sold, or traded.
-
B.
wineTrend
Indicates a relationship where a wine’s popularity, consumption, or market performance is changing over time in a particular direction (e.g., increasing, decreasing, or stable).
-
C.
wineEconomyRole
Indicates the role or function an entity has within the wine-related economy, such as production, distribution, trade, or regulation.
-
D.
wineReputation
Indicates the perceived quality, prestige, or standing of a wine based on expert opinion, consumer perception, or historical recognition.
-
E.
wineCategory
Indicates the classification or type of wine that an entity (such as a specific wine) belongs to.
- F. None of above. chosen
Provenance (4 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_69d8e8d244f8819080eb1f3491300db2 |
completed | April 10, 2026, 12:10 p.m. |
| NER | Named-entity recognition | batch_69e6185795bc8190a63061ca794c0d67 |
completed | April 20, 2026, 12:13 p.m. |
| PD | Predicate disambiguation | batch_69e4dd12303c8190a2027c062b2dff40 |
completed | April 19, 2026, 1:48 p.m. |
| PDg | Predicate description generation | batch_69e4df51ac6c819091ce72b07790ffa6 |
completed | April 19, 2026, 1:57 p.m. |
Created at: April 10, 2026, 1:33 p.m.