Triple
T2647940
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ushuaia |
E53826
|
entity |
| Predicate | ICAOAirportCode |
P419
|
FINISHED |
| Object |
SAWH
SAWH is the ICAO airport code for Ushuaia – Malvinas Argentinas International Airport, the world’s southernmost international airport located in Ushuaia, Argentina.
|
E286274
|
NE FINISHED |
How this triple was built (4 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: SAWH | Statement: [Ushuaia, ICAOAirportCode, SAWH]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: SAWH Context triple: [Ushuaia, ICAOAirportCode, SAWH]
-
A.
SAW
SAW is the IATA airport code for Sabiha Gökçen International Airport, a major international airport serving Istanbul, Turkey.
-
B.
SAU
SAU is an international university established by the South Asian Association for Regional Cooperation (SAARC) in New Delhi, India, focusing on postgraduate and doctoral education and research for students from South Asian countries.
-
C.
SAU
SAU is the three-letter ISO 3166-1 alpha-3 country code assigned to Saudi Arabia.
-
D.
SAA
SAA is the ICAO airline designator for South African Airways, the flag carrier airline of South Africa.
-
E.
SARA
SARA is the common short name for the U.S. Superfund Amendments and Reauthorization Act, which expanded and strengthened the federal Superfund program for cleaning up hazardous waste sites.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: SAWH Triple: [Ushuaia, ICAOAirportCode, SAWH]
Generated description
SAWH is the ICAO airport code for Ushuaia – Malvinas Argentinas International Airport, the world’s southernmost international airport located in Ushuaia, Argentina.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: SAWH Target entity description: SAWH is the ICAO airport code for Ushuaia – Malvinas Argentinas International Airport, the world’s southernmost international airport located in Ushuaia, Argentina.
-
A.
SAW
SAW is the IATA airport code for Sabiha Gökçen International Airport, a major international airport serving Istanbul, Turkey.
-
B.
SAU
SAU is an international university established by the South Asian Association for Regional Cooperation (SAARC) in New Delhi, India, focusing on postgraduate and doctoral education and research for students from South Asian countries.
-
C.
SAU
SAU is the three-letter ISO 3166-1 alpha-3 country code assigned to Saudi Arabia.
-
D.
SAA
SAA is the ICAO airline designator for South African Airways, the flag carrier airline of South Africa.
-
E.
SARA
SARA is the common short name for the U.S. Superfund Amendments and Reauthorization Act, which expanded and strengthened the federal Superfund program for cleaning up hazardous waste sites.
- F. None of above. chosen
Provenance (5 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_69ab495e192081909c77b622e8e7e15a |
completed | March 6, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69abd91b4b3c81908571e85a1621dfc5 |
completed | March 7, 2026, 7:51 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69af98c944548190a8dbe9b81045e97b |
completed | March 10, 2026, 4:06 a.m. |
| NEDg | Description generation | batch_69af99fcd7348190b7e99d58ce9c363a |
completed | March 10, 2026, 4:11 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69af9a5938b48190820f37f2e2280438 |
completed | March 10, 2026, 4:13 a.m. |
Created at: March 6, 2026, 9:53 p.m.