Triple
T15724723
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Neo-Babylonian architecture |
E381188
|
entity |
| Predicate | urbanElement |
P30278
|
FINISHED |
| Object | broad axial streets |
—
|
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: broad axial streets | Statement: [Neo-Babylonian architecture, urbanElement, broad axial streets]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: urbanElement Context triple: [Neo-Babylonian architecture, urbanElement, broad axial streets]
-
A.
urbanComponent
chosen
Indicates that something functions as a constituent part or element within an urban area or city system.
-
B.
urbanDesignElement
Indicates a relationship where something functions as a designed feature or component that shapes the form, use, or experience of an urban environment.
-
C.
refersToUrbanFeature
Indicates that one entity makes reference or points specifically to an urban feature such as a city-related structure, space, or infrastructure element.
-
D.
cityPanorama
Indicates a wide, comprehensive visual view or representation of a cityscape, typically encompassing many of its features in a single scene.
-
E.
cityScene
Indicates a scene or setting that takes place within an urban or city environment.
- 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_69d86d9cdb648190bf3171be0bd7d872 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e04fb1fdd4819088f3e243263e5f73 |
completed | April 16, 2026, 2:55 a.m. |
| PD | Predicate disambiguation | batch_69e00526759c819088b80d85138b8974 |
completed | April 15, 2026, 9:37 p.m. |
Created at: April 10, 2026, 4:46 a.m.