Triple
T953463
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dubai Fountain |
E20573
|
entity |
| Predicate | waterFeatureType |
P20923
|
FINISHED |
| Object | musical fountain |
—
|
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: musical fountain | Statement: [Dubai Fountain, waterFeatureType, musical fountain]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: waterFeatureType Context triple: [Dubai Fountain, waterFeatureType, musical fountain]
-
A.
waterFeature
chosen
Indicates the presence of a natural or artificial body or flow of water associated with the subject.
-
B.
waterType
Indicates the specific kind or category of water associated with an entity (e.g., fresh, salt, brackish).
-
C.
waterbodyType
Indicates the classification of a water body according to its type (e.g., river, lake, ocean, etc.).
-
D.
riverFeatureType
Indicates the specific kind or category of physical or functional feature associated with a river (e.g., source, mouth, tributary, channel segment).
-
E.
waterSource
Indicates that one entity serves as the source or provider of water for another entity.
- 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_69a493b0f2fc81908cd227480a5356a1 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4b3d8f2e0819097554a301f8aa70f |
completed | March 1, 2026, 9:47 p.m. |
| PD | Predicate disambiguation | batch_69a4b2a045308190ab94f3adab40db8d |
completed | March 1, 2026, 9:41 p.m. |
Created at: March 1, 2026, 7:40 p.m.