Triple
T9593695
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1855 Bordeaux classification |
E231478
|
entity |
| Predicate | numberOfClassifiedRedChâteaux |
P89112
|
FINISHED |
| Object | 61 |
—
|
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: 61 | Statement: [1855 Bordeaux classification, numberOfClassifiedRedChâteaux, 61]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfClassifiedRedChâteaux Context triple: [1855 Bordeaux classification, numberOfClassifiedRedChâteaux, 61]
-
A.
numberOfPremierCruClimats
Indicates the count of premier cru climats associated with a given entity.
-
B.
hasGrandCru
Indicates that an entity possesses, is associated with, or includes a wine classified as Grand Cru.
-
C.
ranksVineyards
Indicates an action where an agent evaluates and orders vineyards according to some criterion of quality, preference, or performance.
-
D.
wineClassificationSystem
Indicates a system or scheme used to categorize and organize wines based on defined criteria such as origin, style, or quality.
-
E.
redWinesLabeledAs
Indicates that certain red wines are designated or identified by a particular label or labeling term.
- 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.