Triple
T14669798
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Clifton beaches |
E344478
|
entity |
| Predicate | hasNearbyPropertyType |
P115275
|
FINISHED |
| Object | luxury apartments |
—
|
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: luxury apartments | Statement: [Clifton beaches, hasNearbyPropertyType, luxury apartments]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNearbyPropertyType Context triple: [Clifton beaches, hasNearbyPropertyType, luxury apartments]
-
A.
hasNeighboringBuilding
Indicates that one building is located adjacent to or directly next to another building.
-
B.
hasNearbyDevelopment
Indicates that a development or construction project exists in close physical proximity to the referenced entity.
-
C.
hasNearbyLandUse
Indicates that one land area is located close to another area characterized by a specific type of land use.
-
D.
hasNearbyTownType
Indicates that one entity has, in its vicinity, a town of a specified type or classification.
-
E.
hasNearbyHotel
Indicates that one entity is located close to or within a short distance of a hotel.
- 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_69d822e283fc8190a0e4c235cf880052 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb54ef2908190b189ced65eec434a |
completed | April 14, 2026, 9:44 p.m. |
| PD | Predicate disambiguation | batch_69de6576f0208190aa94d995e797ac38 |
completed | April 14, 2026, 4:04 p.m. |
| PDg | Predicate description generation | batch_69de716c17cc8190aeb85296abee85a7 |
completed | April 14, 2026, 4:55 p.m. |
Created at: April 10, 2026, 1:27 a.m.