Triple
T21683049
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | San Carlos de Bariloche Airport |
E535159
|
entity |
| Predicate | ICAOcode |
P419
|
FINISHED |
| Object | SAZS |
—
|
NE NERFINISHED |
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: SAZS | Statement: [San Carlos de Bariloche Airport, ICAOcode, SAZS]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: SAZS Context triple: [San Carlos de Bariloche Airport, ICAOcode, SAZS]
-
A.
SAZS
chosen
SAZS is the ICAO airport code for San Carlos de Bariloche Airport, a major gateway to the Patagonia region of Argentina.
-
B.
SAZU
SAZU is the national academy of sciences and arts of Slovenia, serving as the country’s leading scholarly and artistic institution.
-
C.
SAEZ
SAEZ is the ICAO airport code for Ministro Pistarini International Airport, the main international gateway serving Buenos Aires, Argentina.
-
D.
ZAZ
ZAZ is the IATA airport code for Zaragoza Airport, a major civilian and military airfield serving the city of Zaragoza in northeastern Spain.
-
E.
ZSAM
ZSAM is the ICAO airport code for Xiamen Gaoqi International Airport in Xiamen, Fujian, China.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0c469b6ec8190aee4cadd1527db91 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69ef96c7c6a08190bbd9a89b8a3b921b |
completed | April 27, 2026, 5:03 p.m. |
Created at: April 16, 2026, 6:43 p.m.