Triple
T30009
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cabernet Sauvignon |
E599
|
entity |
| Predicate | crossType |
P2075
|
FINISHED |
| Object | natural crossing |
—
|
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: natural crossing | Statement: [Cabernet Sauvignon, crossType, natural crossing]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: crossType Context triple: [Cabernet Sauvignon, crossType, natural crossing]
-
A.
crossedBy
Indicates that one entity (typically a path, line, or boundary) is intersected or traversed by another entity.
-
B.
crossesBetween
Indicates that one entity passes from one side of a second entity to the other, traversing the space between two reference points or boundaries associated with that second entity.
-
C.
crossedByRiver
Indicates that a river passes across or through a specified area, feature, or route.
-
D.
borderStraddling
Indicates that something (such as a feature, structure, or area) extends across and occupies territory on both sides of a border between two regions or jurisdictions.
-
E.
bridgeType
Indicates the specific kind or classification of a bridge associated with an entity.
- 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_69a2479dec388190967ba648663442c9 |
completed | Feb. 28, 2026, 1:40 a.m. |
| NER | Named-entity recognition | batch_69a2490d80a0819083bf604c1229e903 |
completed | Feb. 28, 2026, 1:46 a.m. |
| PD | Predicate disambiguation | batch_69a2486eb01881909241540dda28e1ff |
completed | Feb. 28, 2026, 1:44 a.m. |
| PDg | Predicate description generation | batch_69a2490c9c348190bf8536a08415b94a |
completed | Feb. 28, 2026, 1:46 a.m. |
Created at: Feb. 28, 2026, 1:44 a.m.