Triple
T9885329
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nuits-Saint-Georges |
E180916
|
entity |
| Predicate | hasNoGrandCruVineyards |
P91607
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Nuits-Saint-Georges, hasNoGrandCruVineyards, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNoGrandCruVineyards Context triple: [Nuits-Saint-Georges, hasNoGrandCruVineyards, true]
-
A.
hasGrandCru
Indicates that an entity possesses, is associated with, or includes a wine classified as Grand Cru.
-
B.
hasVineyards
Indicates that one entity possesses, contains, or is associated with vineyards used for growing grapevines.
-
C.
hasWinemakingFacility
Indicates that an entity possesses or is associated with a facility where winemaking activities are carried out.
-
D.
hasWinery
Indicates a relationship where a subject owns, operates, or is associated with a particular winery.
-
E.
hasWinemaker
Indicates that one entity serves as the winemaker responsible for producing or overseeing the production of wine for another entity.
- 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_69ca828082cc8190a40f8d299caa6545 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cdb45659748190a3ebd1abe23c8779 |
completed | April 2, 2026, 12:12 a.m. |
| PD | Predicate disambiguation | batch_69cd1d810ed48190a252b70e9390c8f3 |
completed | April 1, 2026, 1:28 p.m. |
| PDg | Predicate description generation | batch_69cd36f112bc81908b473787e702de2f |
completed | April 1, 2026, 3:17 p.m. |
Created at: March 30, 2026, 8:38 p.m.