Triple
T37481859
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Swiss Cattle Dogs |
E931432
|
entity |
| Predicate | typicalCoatType |
P205924
|
FINISHED |
| Object | double coat |
—
|
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: double coat | Statement: [Swiss Cattle Dogs, typicalCoatType, double coat]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalCoatType Context triple: [Swiss Cattle Dogs, typicalCoatType, double coat]
-
A.
garmentType
Indicates the specific kind or category of garment associated with an entity.
-
B.
typicalWear
Indicates that one entity is commonly or characteristically worn by the other in typical situations or contexts.
-
C.
typicalFit
Indicates that one entity is a usual, expected, or characteristic match or correspondence for another in a given context.
-
D.
typicalFabric
Indicates that something is made from or associated with a fabric material that is standard or characteristic for its type.
-
E.
typicalCoatColor
Indicates the usual or most common coat color associated with an entity, such as an animal or breed.
- 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_69f76ec382248190b47844df596123c6 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_6a037cad051c8190b28b354b89208574 |
completed | May 12, 2026, 7:17 p.m. |
| PD | Predicate disambiguation | batch_6a037a13a1308190a202df66f4781855 |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037c842b2c819082f1d2db995ac2eb |
completed | May 12, 2026, 7:16 p.m. |
Created at: May 3, 2026, 4:17 p.m.