Triple
T907192
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Alsace |
E19573
|
entity |
| Predicate | vineyardAreaRankInFrance |
P22574
|
FINISHED |
| Object | one of the major wine regions of France |
—
|
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: one of the major wine regions of France | Statement: [Alsace, vineyardAreaRankInFrance, one of the major wine regions of France]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: vineyardAreaRankInFrance Context triple: [Alsace, vineyardAreaRankInFrance, one of the major wine regions of France]
-
A.
wineRegion
Indicates the geographical region or area where a particular wine is produced or originates.
-
B.
populationRankInFrance
Indicates the relative position of an entity in an ordered list based on its population size within France.
-
C.
nearWineRegion
Indicates that one entity is located close to or in the vicinity of a wine-producing region.
-
D.
viticulturalCharacteristic
Indicates a relationship where a specific trait, quality, or property is attributed to viticulture or grape-growing practices.
-
E.
viticulturalFocus
Indicates a focus on or specialization in viticulture, i.e., activities, practices, or interests centered on grape growing and vineyard management.
- 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_69a4939e889c8190ac148b3ac1a7f90b |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4b3bcad2481908b83575b2fb80d14 |
completed | March 1, 2026, 9:46 p.m. |
| PD | Predicate disambiguation | batch_69a4b28ff5948190982c4439eadf9d87 |
completed | March 1, 2026, 9:41 p.m. |
| PDg | Predicate description generation | batch_69a4b3bab5788190a62a0e23a698f7c7 |
completed | March 1, 2026, 9:46 p.m. |
Created at: March 1, 2026, 7:39 p.m.