Triple
T33437706
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | CityJSON |
E856272
|
entity |
| Predicate | geometryModel |
P44407
|
FINISHED |
| Object | boundary representation (B-Rep) |
—
|
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: boundary representation (B-Rep) | Statement: [CityJSON, geometryModel, boundary representation (B-Rep)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: geometryModel Context triple: [CityJSON, geometryModel, boundary representation (B-Rep)]
-
A.
animationModel
Indicates that one entity serves as the animation model or reference rig used to drive or define the animated behavior of another entity.
-
B.
designModel
chosen
Indicates that one entity creates, specifies, or defines the structure or behavior of another entity as a model or blueprint.
-
C.
geometricFeature
Indicates a relationship where one entity possesses or is characterized by a specific geometric property, shape, or structural feature.
-
D.
drawingModel
Indicates that one entity serves as a drawing or visual representation model used to depict, design, or illustrate another entity.
-
E.
geometricConfiguration
Indicates the specific spatial arrangement and relationships among parts or elements within a geometric structure.
- 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_69f349709e7881908c342b4d34f555f4 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69f6e4876d7881908c5c750db302fcfc |
completed | May 3, 2026, 6 a.m. |
| PD | Predicate disambiguation | batch_69f6e3da41948190a4cfe866ce184f73 |
completed | May 3, 2026, 5:57 a.m. |
Created at: May 1, 2026, 1:36 a.m.