Triple
T23259375
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Banpo Bridge |
E581961
|
entity |
| Predicate | MoonlightRainbowFountainNozzles |
P69407
|
FINISHED |
| Object | approximately 10,000 LED nozzles |
—
|
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: approximately 10,000 LED nozzles | Statement: [Banpo Bridge, MoonlightRainbowFountainNozzles, approximately 10,000 LED nozzles]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: MoonlightRainbowFountainNozzles Context triple: [Banpo Bridge, MoonlightRainbowFountainNozzles, approximately 10,000 LED nozzles]
-
A.
fountainType
Indicates the specific kind or category of fountain associated with an entity.
-
B.
fountainDesigner
Indicates that one entity is the designer or creator of a particular fountain associated with another entity.
-
C.
nozzleType
chosen
Indicates the specific kind or category of nozzle associated with or used by an entity.
-
D.
hasFountain
Indicates that one entity contains, features, or is equipped with a fountain.
-
E.
numberOfFountains
Indicates the quantitative relationship specifying how many fountains are associated with a given 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_69e246079f58819085eaa9c260906880 |
completed | April 17, 2026, 2:39 p.m. |
| NER | Named-entity recognition | batch_69f194c7ec148190b01fd215a0c1daa1 |
completed | April 29, 2026, 5:19 a.m. |
| PD | Predicate disambiguation | batch_69effce4d704819092826931d430e8c4 |
completed | April 28, 2026, 12:18 a.m. |
Created at: April 17, 2026, 4:11 p.m.