Triple
T8628942
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Teatralnaya Square |
E204350
|
entity |
| Predicate | hasUrbanFurniture |
P49960
|
FINISHED |
| Object | benches |
—
|
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: benches | Statement: [Teatralnaya Square, hasUrbanFurniture, benches]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasUrbanFurniture Context triple: [Teatralnaya Square, hasUrbanFurniture, benches]
-
A.
hasStreetFurniture
chosen
Indicates that a location or area contains installed public fixtures such as benches, lamps, bins, or similar street furniture.
-
B.
hasUrbanFabric
Indicates that one entity possesses, contains, or is characterized by a particular pattern or structure of built-up urban development.
-
C.
hasUrbanFeature
Indicates that a place or area possesses a specific urban element or infrastructure feature (such as roads, parks, or buildings) as part of its built environment.
-
D.
containsFurnitureBy
Indicates that one entity includes or holds furniture items that are provided, created, or specified by another entity.
-
E.
hasUrbanFunction
Indicates that an entity serves a specific role or purpose within an urban context, such as providing services, infrastructure, or activities typical of a 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_69ca834a4ea0819094970dceb9e389f3 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5730309081909a9a0256c9bf5f8f |
completed | March 31, 2026, 11:22 p.m. |
| PD | Predicate disambiguation | batch_69cc455906f8819082edd79cb4a1cf28 |
completed | March 31, 2026, 10:06 p.m. |
Created at: March 30, 2026, 6:27 p.m.