Triple
T35945975
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Geranium |
E1039585
|
entity |
| Predicate | fruitFeature |
P18450
|
FINISHED |
| Object | elongated beak-like column |
—
|
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: elongated beak-like column | Statement: [Geranium, fruitFeature, elongated beak-like column]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fruitFeature Context triple: [Geranium, fruitFeature, elongated beak-like column]
-
A.
featuresItem
Indicates that one entity includes, presents, or highlights another entity as a notable item or component.
-
B.
featuresIn
Indicates that an entity appears or plays a role within another entity, such as a person or element being included in a work, event, or context.
-
C.
fruitCharacteristic
chosen
Indicates that a specified characteristic or property is attributed to a particular fruit.
-
D.
brandFeatures
Indicates that a brand includes, offers, or is characterized by a particular feature or attribute.
-
E.
featuresDemon
Indicates that an entity includes, depicts, or prominently involves a demon.
- 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_69f76e24bbd0819096b837d35371639a |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f7b69b333081909cadbed3fcb8ecf5 |
completed | May 3, 2026, 8:56 p.m. |
| PD | Predicate disambiguation | batch_69f7b4c2a5f8819094ad4621d7b97e0c |
completed | May 3, 2026, 8:49 p.m. |
Created at: May 3, 2026, 4:07 p.m.