Triple
T249546
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fort Salonga, New York |
E5111
|
entity |
| Predicate | landUse |
P5777
|
FINISHED |
| Object | primarily residential |
—
|
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: primarily residential | Statement: [Fort Salonga, New York, landUse, primarily residential]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: landUse Context triple: [Fort Salonga, New York, landUse, primarily residential]
-
A.
legalArea
Indicates the specific field or branch of law that a legal matter, case, or document pertains to.
-
B.
urbanDevelopment
Indicates the process or activities through which urban areas are planned, expanded, or transformed, including changes to infrastructure, land use, and the built environment.
-
C.
urbanAreaType
Indicates the classification of an area based on its urban characteristics or development type (e.g., city, town, suburb, metropolitan region).
-
D.
realEstateCategory
chosen
Indicates the classification of a property into a specific type or category within real estate (e.g., residential, commercial, industrial).
-
E.
regionType
Indicates the classification or category of a region, specifying what kind of region it is (e.g., administrative, geographic, or functional).
- 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_69a257c4bf688190a46ebbf411ab7473 |
completed | Feb. 28, 2026, 2:49 a.m. |
| NER | Named-entity recognition | batch_69a25d3728f0819086214ccc2db2305a |
completed | Feb. 28, 2026, 3:12 a.m. |
| PD | Predicate disambiguation | batch_69a25b665f8c8190aac6fcbba2a0eebb |
completed | Feb. 28, 2026, 3:05 a.m. |
Created at: Feb. 28, 2026, 2:54 a.m.