Triple
T7357493
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Doncaster Sheffield Airport |
E169661
|
entity |
| Predicate | ICAOcode |
P419
|
FINISHED |
| Object |
EGCN
EGCN is the ICAO airport code for Doncaster Sheffield Airport, a former international airport in South Yorkshire, England.
|
E658819
|
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: EGCN | Statement: [Doncaster Sheffield Airport, ICAOcode, EGCN]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: EGCN Context triple: [Doncaster Sheffield Airport, ICAOcode, EGCN]
-
A.
DGL
DGL is the vehicle registration code assigned to the town of Głogów in Poland.
-
B.
GRU
GRU is the IATA airport code for São Paulo–Guarulhos International Airport, the main international gateway serving São Paulo, Brazil.
-
C.
GRU
GRU is Russia’s military intelligence agency, known for conducting espionage, cyber operations, and covert activities abroad.
-
D.
Pointer Networks
Pointer Networks are a type of neural network architecture that uses attention mechanisms to output discrete positions in an input sequence, enabling solutions to combinatorial problems like sorting and the traveling salesman problem.
-
E.
FCN
FCN is the common abbreviation for 1. FC Nürnberg, a German football club based in Nuremberg.
- 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: EGCN Triple: [Doncaster Sheffield Airport, ICAOcode, EGCN]
Generated description
EGCN is the ICAO airport code for Doncaster Sheffield Airport, a former international airport in South Yorkshire, England.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: EGCN Target entity description: EGCN is the ICAO airport code for Doncaster Sheffield Airport, a former international airport in South Yorkshire, England.
-
A.
DGL
DGL is the vehicle registration code assigned to the town of Głogów in Poland.
-
B.
GRU
GRU is the IATA airport code for São Paulo–Guarulhos International Airport, the main international gateway serving São Paulo, Brazil.
-
C.
GRU
GRU is Russia’s military intelligence agency, known for conducting espionage, cyber operations, and covert activities abroad.
-
D.
Pointer Networks
Pointer Networks are a type of neural network architecture that uses attention mechanisms to output discrete positions in an input sequence, enabling solutions to combinatorial problems like sorting and the traveling salesman problem.
-
E.
FCN
FCN is the common abbreviation for 1. FC Nürnberg, a German football club based in Nuremberg.
- 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_69c68a59f2288190877ca15c19b1e822 |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f13bf2e881909bc95b93a5664a4f |
completed | March 27, 2026, 9:06 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7faaa8ec8819093bf6b4562472288 |
completed | March 28, 2026, 3:58 p.m. |
| NEDg | Description generation | batch_69c7fc5d7e5081908c9f7389f4dea1e0 |
completed | March 28, 2026, 4:05 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c7fcb5f7f0819081e70f8809bb34ae |
completed | March 28, 2026, 4:07 p.m. |
Created at: March 27, 2026, 3:06 p.m.