Triple
T1588276
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Brasília |
E34115
|
entity |
| Predicate | federalDistrictArea |
P30028
|
FINISHED |
| Object | about 5,802 km² |
—
|
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: about 5,802 km² | Statement: [Brasília, federalDistrictArea, about 5,802 km²]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: federalDistrictArea Context triple: [Brasília, federalDistrictArea, about 5,802 km²]
-
A.
landAreaSquareMiles
Indicates the size of a geographic area measured in square miles.
-
B.
areaTotalSquareMiles
Indicates the total geographic area of something measured in square miles.
-
C.
includesFederalDistrict
Indicates that one administrative or geographic entity contains or encompasses a federal district within its boundaries.
-
D.
areaOfMemberStatesApprox
Indicates the approximate total geographic area collectively covered by the member states of a given organization or grouping.
-
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_69a885fceb2c8190b47e0f7c0aefbff0 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a93aedd45c819085843ac843d640e8 |
completed | March 5, 2026, 8:12 a.m. |
| PD | Predicate disambiguation | batch_69a907bdc19081908c84c5c0aa09e282 |
completed | March 5, 2026, 4:34 a.m. |
| PDg | Predicate description generation | batch_69a93aec7dc481909375726fbfb9e272 |
completed | March 5, 2026, 8:12 a.m. |
Created at: March 4, 2026, 7:27 p.m.