Triple
T8106039
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ministro Pistarini International Airport |
E189227
|
entity |
| Predicate | icaoCode |
P419
|
FINISHED |
| Object | SAEZ |
E189227
|
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: SAEZ | Statement: [Ministro Pistarini International Airport, icaoCode, SAEZ]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: SAEZ Context triple: [Ministro Pistarini International Airport, icaoCode, SAEZ]
-
A.
SAEZ
chosen
SAEZ is the ICAO airport code for Ministro Pistarini International Airport, the main international gateway serving Buenos Aires, Argentina.
-
B.
SAZS
SAZS is the ICAO airport code for San Carlos de Bariloche Airport, a major gateway to the Patagonia region of Argentina.
-
C.
SAZU
SAZU is the national academy of sciences and arts of Slovenia, serving as the country’s leading scholarly and artistic institution.
-
D.
SA3
SA3 is the 3GPP security working group responsible for specifying and evolving security architecture and mechanisms across mobile communication standards.
-
E.
SA8
SA8 is an Amway laundry detergent brand known for its concentrated, environmentally conscious cleaning products.
- 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_69ca82b9d5848190a24672775d5c5011 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb42f735c8819090d0d822644c0a51 |
completed | March 31, 2026, 3:43 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cc941075588190a9b12e2f873ba2a5 |
completed | April 1, 2026, 3:42 a.m. |
Created at: March 30, 2026, 5:31 p.m.