Triple
T10412971
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | South African Airways |
E245437
|
entity |
| Predicate | hasICAOCode |
P419
|
FINISHED |
| Object | SAA |
E245437
|
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: SAA | Statement: [South African Airways, hasICAOCode, SAA]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: SAA Context triple: [South African Airways, hasICAOCode, SAA]
-
A.
SAA
chosen
SAA is the ICAO airline designator for South African Airways, the flag carrier airline of South Africa.
-
B.
SAA
SAA is the National Rail station code for St Albans Abbey railway station in Hertfordshire, England.
-
C.
SAA
The SAA is the main military force of the Syrian Arab Republic, responsible for land-based defense and combat operations.
-
D.
SAAWK
SAAWK is the abbreviation for the Suid-Afrikaanse Akademie vir Wetenskap en Kuns, a South African academy dedicated to promoting Afrikaans language, science, and the arts.
-
E.
SSA
SSA is a professional scientific organization dedicated to advancing the study and understanding of earthquakes and seismic phenomena.
- 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_69d381be340c8190b05998703d42d224 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4ea0e17f081908fb16425f65e5808 |
completed | April 7, 2026, 11:27 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d87e9b86648190b83eb5261c9a7b97 |
completed | April 10, 2026, 4:37 a.m. |
Created at: April 6, 2026, 12:10 p.m.