Triple
T2797419
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lanseria International Airport |
E53071
|
entity |
| Predicate | ICAO code |
P419
|
FINISHED |
| Object |
FALA
FALA is the ICAO airport code for Lanseria International Airport, a major privately owned international airport serving the Johannesburg region in South Africa.
|
E299488
|
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: FALA | Statement: [Lanseria International Airport, ICAO code, FALA]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: FALA Context triple: [Lanseria International Airport, ICAO code, FALA]
-
A.
Follaz
Follaz is a tributary stream of the Dranse river in the Alpine region of eastern France.
-
B.
Foles
Foles is the surname of Nick Foles, an American football quarterback best known for leading the Philadelphia Eagles to a Super Bowl LII victory and earning the game's MVP award.
-
C.
Flen
Flen is a small Swedish town known as the administrative center of Flen Municipality in the province of Södermanland.
-
D.
F.A.C.
F.A.C. is the standard legal abbreviation used to refer to the Florida Administrative Code, which contains the administrative rules and regulations of the state of Florida.
-
E.
Fay
Fay is a given name most famously associated with Canadian-American actress Fay Wray, the iconic star of the 1933 film "King Kong."
- 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: FALA Triple: [Lanseria International Airport, ICAO code, FALA]
Generated description
FALA is the ICAO airport code for Lanseria International Airport, a major privately owned international airport serving the Johannesburg region in South Africa.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: FALA Target entity description: FALA is the ICAO airport code for Lanseria International Airport, a major privately owned international airport serving the Johannesburg region in South Africa.
-
A.
Follaz
Follaz is a tributary stream of the Dranse river in the Alpine region of eastern France.
-
B.
Foles
Foles is the surname of Nick Foles, an American football quarterback best known for leading the Philadelphia Eagles to a Super Bowl LII victory and earning the game's MVP award.
-
C.
Flen
Flen is a small Swedish town known as the administrative center of Flen Municipality in the province of Södermanland.
-
D.
F.A.C.
F.A.C. is the standard legal abbreviation used to refer to the Florida Administrative Code, which contains the administrative rules and regulations of the state of Florida.
-
E.
Fay
Fay is a given name most famously associated with Canadian-American actress Fay Wray, the iconic star of the 1933 film "King Kong."
- 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_69ab495a90788190941b6917e1eca3a6 |
completed | March 6, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69abddf0f4988190bffc3abab7edbb81 |
completed | March 7, 2026, 8:12 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69afc66798148190bd7b163043167409 |
completed | March 10, 2026, 7:21 a.m. |
| NEDg | Description generation | batch_69afc6e6d4bc81908108fe24677448c3 |
completed | March 10, 2026, 7:23 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69afc792051c8190b8c95156e5fcfe2f |
completed | March 10, 2026, 7:26 a.m. |
Created at: March 6, 2026, 9:58 p.m.