Triple
T953470
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dubai Fountain |
E20573
|
entity |
| Predicate | numberOfNozzles |
P21647
|
FINISHED |
| Object | over 6000 smaller lights and nozzles combined |
—
|
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: over 6000 smaller lights and nozzles combined | Statement: [Dubai Fountain, numberOfNozzles, over 6000 smaller lights and nozzles combined]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfNozzles Context triple: [Dubai Fountain, numberOfNozzles, over 6000 smaller lights and nozzles combined]
-
A.
numberOfPumps
Indicates the quantity of pumps associated with or required by an entity or system.
-
B.
numberOfCanons
Indicates the quantity of canons associated with or possessed by a given entity.
-
C.
numberOfTubes
Indicates the quantity of tubes associated with or contained by a given entity.
-
D.
numberOfFountains
Indicates the quantitative relationship specifying how many fountains are associated with a given entity.
-
E.
numberOfEngines
Indicates the quantity of engines associated with or used by 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_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. |
| PDg | Predicate description generation | batch_69a4b30efd2c8190b780a6dee086d0aa |
completed | March 1, 2026, 9:43 p.m. |
Created at: March 1, 2026, 7:40 p.m.