Triple
T14737489
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Manaus Air Base |
E346251
|
entity |
| Predicate | location |
P40
|
FINISHED |
| Object | Amazonas |
E367132
|
NE 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: Amazonas | Statement: [Manaus Air Base, location, Amazonas]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Amazonas Context triple: [Manaus Air Base, location, Amazonas]
-
A.
Amazonas
chosen
Amazonas is a vast state in northwestern Brazil, largely covered by the Amazon rainforest and known for its immense biodiversity and the city of Manaus.
-
B.
Amazonas
Amazonas is a vast, sparsely populated department in southern Colombia known for its dense Amazon rainforest, rich biodiversity, and access to the Amazon River.
-
C.
Amaszonas
Amaszonas is a Bolivian regional airline that operates domestic and short-haul international flights across South America.
-
D.
Amazon River
The Amazon River is one of the world's longest and largest rivers by discharge, flowing across northern South America through the Amazon rainforest and into the Atlantic Ocean.
-
E.
Rio Negro
Rio Negro is a river in southern Brazil that flows through the state of Santa Catarina and serves as an important regional waterway.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d822e6f1c88190bc494d491a907114 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69dec73264848190be23c5f0260cbe13 |
completed | April 14, 2026, 11:01 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fdfb90378481909a3083680f11101c |
completed | May 8, 2026, 3:04 p.m. |
Created at: April 10, 2026, 1:29 a.m.