Triple
T3807643
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Patrimonio |
E93046
|
entity |
| Predicate | typicalWhiteCharacteristics |
P5084
|
FINISHED |
| Object | aromatic |
—
|
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: aromatic | Statement: [Patrimonio, typicalWhiteCharacteristics, aromatic]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalWhiteCharacteristics Context triple: [Patrimonio, typicalWhiteCharacteristics, aromatic]
-
A.
typicalColorDescription
Indicates the usual or characteristic color associated with an entity.
-
B.
typicalFeatures
chosen
Indicates that the related entities are characteristic or commonly occurring features or attributes of something.
-
C.
typicalTexture
Indicates the usual or characteristic surface feel or consistency that is commonly associated with an entity.
-
D.
typicalCoatColor
Indicates the usual or most common coat color associated with an entity, such as an animal or breed.
-
E.
typicalAppearanceContext
Indicates the usual situation, setting, or context in which an entity most commonly appears or is typically encountered.
- 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_69aed96a60088190ab1df8390fffc935 |
completed | March 9, 2026, 2:30 p.m. |
| NER | Named-entity recognition | batch_69aee8db8a288190afd1e3b9dcf02e97 |
completed | March 9, 2026, 3:35 p.m. |
| PD | Predicate disambiguation | batch_69aee7461abc8190945716f4b93e1a18 |
completed | March 9, 2026, 3:29 p.m. |
Created at: March 9, 2026, 3:16 p.m.