Triple
T2635445
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Azul Brazilian Airlines |
E59733
|
entity |
| Predicate | tickerSymbol |
P1447
|
FINISHED |
| Object | AZUL4 |
E283416
|
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: AZUL4 | Statement: [Azul Brazilian Airlines, tickerSymbol, AZUL4]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: AZUL4 Context triple: [Azul Brazilian Airlines, tickerSymbol, AZUL4]
-
A.
AZUL
chosen
AZUL is a Brazilian low-cost airline known for its extensive domestic network and vibrant blue branding.
-
B.
Blau
The Blau is a small river in the German state of Baden-Württemberg that flows through the city of Blaustein before joining the Danube.
-
C.
AZU
AZU is the ICAO airline designator for Azul Brazilian Airlines, a major low-cost carrier based in Brazil.
-
D.
the Blue
The Blue is the traditional nickname and spirit symbol representing Phillips Academy Andover’s athletic teams and school community.
-
E.
Sky Blue
"Sky Blue" is an abstract painting by Russian artist Wassily Kandinsky, characterized by its vibrant colors and dynamic geometric forms that exemplify his pioneering work in non-representational art.
- 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_69ab4ac8596c8190b34997e73d9e991c |
completed | March 6, 2026, 9:44 p.m. |
| NER | Named-entity recognition | batch_69abd8e085b0819089db4103c0d8cd9b |
completed | March 7, 2026, 7:50 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69af98bb55f08190bb072c9b106aa748 |
completed | March 10, 2026, 4:06 a.m. |
Created at: March 6, 2026, 9:50 p.m.