Triple
T235097
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | City of Fountains |
E4491
|
entity |
| Predicate | describesFeature |
P5084
|
FINISHED |
| Object | abundance of decorative public fountains |
—
|
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: abundance of decorative public fountains | Statement: [City of Fountains, describesFeature, abundance of decorative public fountains]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: describesFeature Context triple: [City of Fountains, describesFeature, abundance of decorative public fountains]
-
A.
technologicalFeature
Indicates that one entity possesses, exhibits, or is characterized by a specific technological capability, component, or functionality in relation to another entity.
-
B.
featuresText
Indicates that an entity includes or presents a specific piece of text as one of its characteristics or contents.
-
C.
featureType
Indicates the specific kind or category of feature that characterizes or distinguishes an entity.
-
D.
typicalFeatures
chosen
Indicates that the related entities are characteristic or commonly occurring features or attributes of something.
-
E.
describes
Indicates that one entity provides an explanation, representation, or account of another entity or concept.
- 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_69a257363ffc81909757bde7ab3404da |
completed | Feb. 28, 2026, 2:47 a.m. |
| NER | Named-entity recognition | batch_69a25cc9ab2c81909af278a07f86aa1e |
completed | Feb. 28, 2026, 3:11 a.m. |
| PD | Predicate disambiguation | batch_69a25b5dc640819092669575731c393f |
completed | Feb. 28, 2026, 3:05 a.m. |
Created at: Feb. 28, 2026, 2:53 a.m.