Triple
T31140303
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Northern Rome |
E793761
|
entity |
| Predicate | urbanPerimeter |
P173764
|
FINISHED |
| Object | upper urban perimeter of Rome |
—
|
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: upper urban perimeter of Rome | Statement: [Northern Rome, urbanPerimeter, upper urban perimeter of Rome]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: urbanPerimeter Context triple: [Northern Rome, urbanPerimeter, upper urban perimeter of Rome]
-
A.
urbanAreaType
Indicates the classification of an area based on its urban characteristics or development type (e.g., city, town, suburb, metropolitan region).
-
B.
withinUrbanArea
Indicates that one entity is located inside the spatial boundaries of an urban area associated with another entity.
-
C.
urbanScale
Indicates the extent or magnitude of development, density, or size at which an area or environment can be characterized as urban.
-
D.
urbanLandscapeCharacterizedBy
Indicates that an urban landscape possesses or is defined by specific distinguishing features, qualities, or elements.
-
E.
urbanDistrictType
Indicates the classification of an urban district according to its specific type or category within an administrative or planning system.
- 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_69f224d2b3a48190aa9dd26fbf6eab1a |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f6ba1733408190af579d93a7946508 |
completed | May 3, 2026, 2:59 a.m. |
| PD | Predicate disambiguation | batch_69f6b6293188819080d5041ca0adb969 |
completed | May 3, 2026, 2:42 a.m. |
| PDg | Predicate description generation | batch_69f6b960ca4081909a77690c2b122f5e |
completed | May 3, 2026, 2:56 a.m. |
Created at: April 29, 2026, 9:05 p.m.