Triple
T16929043
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fountain Hills, Arizona |
E410653
|
entity |
| Predicate | fountainType |
P124786
|
FINISHED |
| Object | man-made 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: man-made fountain | Statement: [Fountain Hills, Arizona, fountainType, man-made fountain]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fountainType Context triple: [Fountain Hills, Arizona, fountainType, man-made fountain]
-
A.
fountainDesigner
Indicates that one entity is the designer or creator of a particular fountain associated with another entity.
-
B.
hasFountain
Indicates that one entity contains, features, or is equipped with a fountain.
-
C.
numberOfFountains
Indicates the quantitative relationship specifying how many fountains are associated with a given entity.
-
D.
terminusFountain
Indicates that one entity serves as the fountain or source point at the terminus (end point) of another entity.
-
E.
waterType
Indicates the specific kind or category of water associated with an entity (e.g., fresh, salt, brackish).
- 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_69d886c886688190967be07322597ac9 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3cf23873c8190bdc9121d6c3850e2 |
completed | April 18, 2026, 6:36 p.m. |
| PD | Predicate disambiguation | batch_69e32b982f548190b08414d55810de19 |
completed | April 18, 2026, 6:58 a.m. |
| PDg | Predicate description generation | batch_69e32d7aae948190bc238d765795688c |
completed | April 18, 2026, 7:06 a.m. |
Created at: April 10, 2026, 5:30 a.m.