Triple
T9623575
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Château Haut-Bailly |
E232402
|
entity |
| Predicate | doesNotTypicallyProduce |
P23761
|
FINISHED |
| Object | white wine |
—
|
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: white wine | Statement: [Château Haut-Bailly, doesNotTypicallyProduce, white wine]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: doesNotTypicallyProduce Context triple: [Château Haut-Bailly, doesNotTypicallyProduce, white wine]
-
A.
doesNotProduce
chosen
Indicates that one entity fails to generate, create, or yield another entity or outcome.
-
B.
notTypicallyUsedFor
Indicates that something is generally not used for a particular purpose, function, or activity under normal circumstances.
-
C.
doesNotUse
Indicates that one entity intentionally refrains from employing, utilizing, or relying on another entity, method, or resource.
-
D.
doesNotServe
Indicates that one entity explicitly does not provide a service, function, or role to another entity.
-
E.
doesNot
Indicates that a specified entity lacks, refrains from, or fails to perform a particular action or exhibit a particular property in relation to another entity or context.
- 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_69ca848793ec8190a93a12383a754dc0 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd9ad650a4819096258665bc3f410b |
completed | April 1, 2026, 10:23 p.m. |
| PD | Predicate disambiguation | batch_69ccd5aa1d2c8190a287bf1cf4a3037e |
completed | April 1, 2026, 8:22 a.m. |
Created at: March 30, 2026, 8:10 p.m.