Triple
T3693700
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | College Park Aviation Museum |
E78402
|
entity |
| Predicate | hasApproximateFloorArea |
P24212
|
FINISHED |
| Object | 27000 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: 27000 square feet | Statement: [College Park Aviation Museum, hasApproximateFloorArea, 27000 square feet]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasApproximateFloorArea Context triple: [College Park Aviation Museum, hasApproximateFloorArea, 27000 square feet]
-
A.
hasFloorArea
chosen
Indicates that an entity possesses a specified amount of floor space as a measurable area.
-
B.
roofArea
Indicates the total surface area covered by the roof of a structure.
-
C.
grossLeasableArea
Indicates the total floor area within a property that is available to be leased to tenants, excluding common or non-leasable spaces.
-
D.
areaApprox
Indicates that one entity’s area is approximately equal to the area of another entity.
-
E.
hasCasinoFloorArea
Indicates the total floor area occupied by a casino within a given property or facility.
- 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_69ad85e3b1888190abc983e06968696d |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc4e9e4748190aa178692ef27e3a6 |
completed | March 8, 2026, 6:50 p.m. |
| PD | Predicate disambiguation | batch_69adb84dc5808190850aa6975cb09e27 |
completed | March 8, 2026, 5:56 p.m. |
Created at: March 8, 2026, 3:26 p.m.