Triple
T9386661
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Château Pape Clément |
E225920
|
entity |
| Predicate | vineyardArea |
P44612
|
FINISHED |
| Object | approximately 30 hectares (red) |
—
|
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: approximately 30 hectares (red) | Statement: [Château Pape Clément, vineyardArea, approximately 30 hectares (red)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: vineyardArea Context triple: [Château Pape Clément, vineyardArea, approximately 30 hectares (red)]
-
A.
vineyardSize
chosen
Indicates the extent or area of land occupied by a vineyard.
-
B.
viticulturalAreaType
Indicates the specific type or classification of a viticultural area associated with an entity.
-
C.
viticulturalAreaCode
Indicates the designated code that identifies the specific viticultural (wine-producing) area associated with an entity.
-
D.
vineyardAreaRankInFrance
Indicates the relative position of an entity’s vineyard area compared to other vineyard areas within France, ordered by size.
-
E.
hasVineyards
Indicates that one entity possesses, contains, or is associated with vineyards used for growing grapevines.
- 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_69ca842e9dcc8190a264119e683cfe04 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd50d3964c8190b0353f56df755db8 |
completed | April 1, 2026, 5:07 p.m. |
| PD | Predicate disambiguation | batch_69cca53bd6ec81909bf403ce304e5c08 |
completed | April 1, 2026, 4:55 a.m. |
Created at: March 30, 2026, 7:45 p.m.