Triple
T8528144
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | La Tâche AOC |
E201870
|
entity |
| Predicate | belongsToWineLawSystem |
P12605
|
FINISHED |
| Object | French AOC system |
—
|
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: French AOC system | Statement: [La Tâche AOC, belongsToWineLawSystem, French AOC system]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: belongsToWineLawSystem Context triple: [La Tâche AOC, belongsToWineLawSystem, French AOC system]
-
A.
hasOwnCanonLawParticularLaw
Indicates that an entity possesses its own specific set of canon or particular laws distinct from general or universal church law.
-
B.
hasLegalSystemType
Indicates that an entity possesses or is governed by a particular type or form of legal system.
-
C.
relatedLegalSystem
Indicates that there is an association or connection between two legal systems, such as influence, similarity, shared origin, or mutual relevance.
-
D.
countryOfLegalSystem
Indicates the relationship between a legal system and the country in which that legal system is officially established or applied.
-
E.
governedByLegalRegime
chosen
Indicates that an entity is subject to, regulated by, or operating under a specific legal framework or set of legal rules.
- 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_69ca83228b24819085d22e7dc99f5d94 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe672e0588190a84328e1bf974f08 |
completed | March 31, 2026, 3:21 p.m. |
| PD | Predicate disambiguation | batch_69cbd111bf988190be98c92a607c6456 |
completed | March 31, 2026, 1:50 p.m. |
Created at: March 30, 2026, 6:17 p.m.