Triple
T2635446
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Azul Brazilian Airlines |
E59733
|
entity |
| Predicate | tickerSymbol |
P1447
|
FINISHED |
| Object | AZUL |
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: AZUL | Statement: [Azul Brazilian Airlines, tickerSymbol, AZUL]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: AZUL Context triple: [Azul Brazilian Airlines, tickerSymbol, AZUL]
-
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.
Trucolor
Trucolor was a mid-20th-century color motion picture process developed by Republic Pictures, used primarily in their Westerns and adventure films.
-
D.
The Blue and White
The Blue and White is a popular nickname for the Toronto Maple Leafs, the historic NHL franchise known for its blue-and-white team colors.
-
E.
Indigo
Indigo is a publishing imprint of the Orion Publishing Group, known for releasing a range of contemporary fiction and non-fiction titles.
- 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_69afa04f0d448190adf113831fb5bc42 |
completed | March 10, 2026, 4:38 a.m. |
Created at: March 6, 2026, 9:50 p.m.