Triple
T98696
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Roosevelt Field |
E1990
|
entity |
| Predicate | currentLandUse |
P2022
|
FINISHED |
| Object | shopping mall |
—
|
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: shopping mall | Statement: [Roosevelt Field, currentLandUse, shopping mall]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: currentLandUse Context triple: [Roosevelt Field, currentLandUse, shopping mall]
-
A.
regionType
Indicates the classification or category of a region, specifying what kind of region it is (e.g., administrative, geographic, or functional).
-
B.
hasLandCoverage
chosen
Indicates that a specified area or region is covered or occupied by a particular type of land surface or land use.
-
C.
urbanAreaType
Indicates the classification of an area based on its urban characteristics or development type (e.g., city, town, suburb, metropolitan region).
-
D.
landArea
Indicates the total surface area of a piece of land associated with an entity, typically measured in standardized units (e.g., square meters, hectares).
-
E.
vegetationType
Indicates the specific kind or category of plant cover or flora that characterizes a given area or 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_69a24d4862f881908cc8b89d3a78031d |
completed | Feb. 28, 2026, 2:04 a.m. |
| NER | Named-entity recognition | batch_69a24ff07d148190a59aee12c807659d |
completed | Feb. 28, 2026, 2:16 a.m. |
| PD | Predicate disambiguation | batch_69a24ebe7b1c8190a6bfbf31dc7c7f07 |
completed | Feb. 28, 2026, 2:11 a.m. |
Created at: Feb. 28, 2026, 2:09 a.m.