Triple
T31534967
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | La Chapelle de La Mission Haut-Brion (second wine) |
E804579
|
entity |
| Predicate | tastingNoteTypical |
P2068
|
FINISHED |
| Object | red and black fruits |
—
|
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: red and black fruits | Statement: [La Chapelle de La Mission Haut-Brion (second wine), tastingNoteTypical, red and black fruits]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: tastingNoteTypical Context triple: [La Chapelle de La Mission Haut-Brion (second wine), tastingNoteTypical, red and black fruits]
-
A.
notableFlavorNotes
Indicates that something is characterized by specific, distinguishable flavor notes that are especially prominent or noteworthy.
-
B.
typicalAromaIntensity
Indicates the usual strength or level of aroma typically associated with something.
-
C.
wineCharacteristic
Indicates a descriptive property or quality attributed to a wine, such as its flavor, aroma, color, or style.
-
D.
typicalFlavor
chosen
Indicates that something characteristically has or is associated with a particular flavor.
-
E.
hasTastingProfile
Indicates that an entity possesses a specific flavor or sensory profile, typically describing its characteristic tastes and aromas.
- F. None of above.
Provenance (3 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_69f348d03ef88190a2b73d7b94b9e02d |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69f6a8055f8081908f635fe04654b5fe |
completed | May 3, 2026, 1:42 a.m. |
| PD | Predicate disambiguation | batch_69f6a75656e081908739ed9e2f600e42 |
completed | May 3, 2026, 1:39 a.m. |
Created at: April 30, 2026, 10:03 p.m.