Triple
T18316733
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Amazonas (Brazilian state) |
E438768
|
entity |
| Predicate | largestBrazilianStateByArea |
P2783
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Amazonas (Brazilian state), largestBrazilianStateByArea, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: largestBrazilianStateByArea Context triple: [Amazonas (Brazilian state), largestBrazilianStateByArea, true]
-
A.
largestPopulationInBrazilianState
Indicates that the subject has the largest population among all entities within a specified Brazilian state.
-
B.
largestStateByArea
chosen
Indicates that a state is the one with the greatest land area within a specified set or region.
-
C.
largestCountyByArea
Indicates that one county has the greatest land area compared to all other counties within a specified region or set.
-
D.
largestMunicipalityByArea
Indicates that the subject is the municipality with the greatest land area within the specified region or set of municipalities.
-
E.
largestRegionByAreaIn
Indicates that one region is the largest by geographic area among all regions within a specified containing area.
- 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_69d8b916a2d081909e249e4902f6aad9 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e5021e61008190a300b6c51976a837 |
completed | April 19, 2026, 4:26 p.m. |
| PD | Predicate disambiguation | batch_69e44fe4ee10819086b4142444fca1f5 |
completed | April 19, 2026, 3:45 a.m. |
Created at: April 10, 2026, 10:36 a.m.