Triple
T36527162
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Château Tour Léognan (red) |
E900336
|
entity |
| Predicate | isSecondWineOf |
P44613
|
FINISHED |
| Object | Château Carbonnieux (red) |
—
|
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: Château Carbonnieux (red) | Statement: [Château Tour Léognan (red), isSecondWineOf, Château Carbonnieux (red)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isSecondWineOf Context triple: [Château Tour Léognan (red), isSecondWineOf, Château Carbonnieux (red)]
-
A.
secondaryWine
chosen
Indicates a relationship where one wine is designated as a secondary or supporting wine in relation to a primary wine.
-
B.
secondaryGrape
Indicates that one grape variety serves as a secondary or supporting component in a wine blend relative to the primary grape.
-
C.
hasSparklingWine
Indicates that an entity possesses, includes, or is associated with sparkling wine.
-
D.
sparklingWineAllowed
Indicates that the use, serving, or presence of sparkling wine is permitted in the given context or under specified conditions.
-
E.
hasWinemakingOption
Indicates that an entity offers or includes a particular method, feature, or configuration related to winemaking.
- 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_69f76e5eedb88190a393b8c623f71dd7 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69f7c371931c8190afb1d4dd5157f92c |
completed | May 3, 2026, 9:51 p.m. |
| PD | Predicate disambiguation | batch_69f7c1b91fd88190ab85afd626603769 |
completed | May 3, 2026, 9:44 p.m. |
Created at: May 3, 2026, 4:11 p.m.