Triple
T625686
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Roosevelt Field Mall |
E15812
|
entity |
| Predicate | grossLeasableArea |
P17847
|
FINISHED |
| Object | over 2,000,000 square feet |
—
|
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: over 2,000,000 square feet | Statement: [Roosevelt Field Mall, grossLeasableArea, over 2,000,000 square feet]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: grossLeasableArea Context triple: [Roosevelt Field Mall, grossLeasableArea, over 2,000,000 square feet]
-
A.
area
Indicates that one entity has a measured two-dimensional extent or surface size quantified by another entity.
-
B.
hasBalcony
Indicates that a building or dwelling includes a balcony as part of its structure or features.
-
C.
hasAreaType
Indicates that an entity is associated with a specific kind or classification of area (e.g., urban, rural, coastal).
-
D.
areaApprox
Indicates that one entity’s area is approximately equal to the area of another entity.
-
E.
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).
- 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_69a4935c131c8190a5378c6bf101e8cc |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a49e574444819087999404f3e3ffd9 |
completed | March 1, 2026, 8:15 p.m. |
| PD | Predicate disambiguation | batch_69a49d0069d0819087c83b608f6fc053 |
completed | March 1, 2026, 8:09 p.m. |
| PDg | Predicate description generation | batch_69a49e2107548190af0c1d67cfa475d1 |
completed | March 1, 2026, 8:14 p.m. |
Created at: March 1, 2026, 7:35 p.m.