Triple
T32023733
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Borderies (Cognac cru) |
E817761
|
entity |
| Predicate | legalProduct |
P197986
|
FINISHED |
| Object | Cognac |
—
|
NE NERFINISHED |
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: Cognac | Statement: [Borderies (Cognac cru), legalProduct, Cognac]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: legalProduct Context triple: [Borderies (Cognac cru), legalProduct, Cognac]
-
A.
licenseOfProduct
Indicates that a specified license applies to, governs, or is associated with a particular product.
-
B.
legalCover
Indicates that one entity provides legal protection, justification, or a façade of legitimacy for another entity’s actions or status.
-
C.
legalLicenseIn
Indicates that an entity holds a valid legal license or authorization to operate, practice, or conduct a specified activity within a particular jurisdiction or region.
-
D.
licensedProductType
Indicates the type or category of product for which a license has been granted.
-
E.
legalContent
Indicates that the associated material complies with applicable laws and regulations and is permitted for use, distribution, or display.
- 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_69f348fb04e4819081f4eab040ed7959 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69fec00f27988190955de6b6348a4d97 |
completed | May 9, 2026, 5:03 a.m. |
| PD | Predicate disambiguation | batch_69febd52037c8190b475dbd50fdbc13e |
completed | May 9, 2026, 4:51 a.m. |
| PDg | Predicate description generation | batch_69fec00e5c1c819083d174bb4bc35f29 |
completed | May 9, 2026, 5:03 a.m. |
Created at: May 1, 2026, 12:17 a.m.