Triple
T9593696
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1855 Bordeaux classification |
E231478
|
entity |
| Predicate | numberOfClassifiedSauternesBarsacEstates |
P89113
|
FINISHED |
| Object | 27 |
—
|
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: 27 | Statement: [1855 Bordeaux classification, numberOfClassifiedSauternesBarsacEstates, 27]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfClassifiedSauternesBarsacEstates Context triple: [1855 Bordeaux classification, numberOfClassifiedSauternesBarsacEstates, 27]
-
A.
hasGrandCru
Indicates that an entity possesses, is associated with, or includes a wine classified as Grand Cru.
-
B.
numberOfPremierCruClimats
Indicates the count of premier cru climats associated with a given entity.
-
C.
ranksVineyards
Indicates an action where an agent evaluates and orders vineyards according to some criterion of quality, preference, or performance.
-
D.
approxNumberOfWineries
Indicates an estimated or approximate count of wineries associated with a given entity.
-
E.
BanyulsGrandCruMinimumAlcohol
Indicates that a Banyuls Grand Cru wine meets or exceeds the specified minimum alcohol content required for that designation.
- 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_69ca8482884481908eccdfdf64d6fbf7 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd9a134b0c81908a568e5d2ecfbb92 |
completed | April 1, 2026, 10:20 p.m. |
| PD | Predicate disambiguation | batch_69ccd5a359788190b24f82399489f7fe |
completed | April 1, 2026, 8:21 a.m. |
| PDg | Predicate description generation | batch_69ccd93fc45c8190a823305e461e581d |
completed | April 1, 2026, 8:37 a.m. |
Created at: March 30, 2026, 8:07 p.m.