Triple
T2186118
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | King Fahd Fountain |
E49155
|
entity |
| Predicate | numberOfLights |
P37175
|
FINISHED |
| Object | over 500 spotlights |
—
|
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 500 spotlights | Statement: [King Fahd Fountain, numberOfLights, over 500 spotlights]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfLights Context triple: [King Fahd Fountain, numberOfLights, over 500 spotlights]
-
A.
hasNumberOfMainLights
Indicates the relationship that specifies how many primary or main lights are associated with an entity.
-
B.
numberOfChandeliers
Indicates the quantity of chandeliers associated with a given entity or context.
-
C.
hasNumberOfShamashLights
Indicates the relationship specifying how many Shamash (helper) lights are present or associated with an object or setting.
-
D.
hasRunwayLighting
Indicates that a runway is equipped with lighting systems to aid visibility and operations, typically during low-light or night conditions.
-
E.
hasOnboardLEDs
Indicates that one entity is equipped with built-in LED lights as part of its hardware.
- 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_69a88aa72d348190a9544bb5b8a4e71d |
completed | March 4, 2026, 7:40 p.m. |
| NER | Named-entity recognition | batch_69abbf9e99f08190892d34485c8f2f25 |
completed | March 7, 2026, 6:03 a.m. |
| PD | Predicate disambiguation | batch_69abbda32d1881909d1fd83a751fb21c |
completed | March 7, 2026, 5:54 a.m. |
| PDg | Predicate description generation | batch_69abbf9c77fc8190a323bcaf644fb2c5 |
completed | March 7, 2026, 6:03 a.m. |
Created at: March 4, 2026, 7:45 p.m.