Triple
T4364315
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Farnborough Airfield |
E98735
|
entity |
| Predicate | hasIATAcode |
P2569
|
FINISHED |
| Object |
FAB
FAB is the IATA airport code for Farnborough Airport, a business aviation airport in Hampshire, England.
|
E432877
|
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: FAB | Statement: [Farnborough Airfield, hasIATAcode, FAB]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: FAB Context triple: [Farnborough Airfield, hasIATAcode, FAB]
-
A.
FAB
FAB is the acronym for the Brazilian Air Force, the aerial warfare branch of Brazil’s armed forces.
-
B.
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.
-
C.
FAR
FAR is the acronym for Cuba’s national military organization, the Revolutionary Armed Forces.
-
D.
FAR
FAR is the commonly used abbreviation for the Intergovernmental Panel on Climate Change’s First Assessment Report, a foundational scientific evaluation of climate change published in 1990.
-
E.
FAR
FAR is the station code for Faro railway station, a key rail transport hub serving the city of Faro in southern Portugal’s Algarve region.
- 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: FAB Triple: [Farnborough Airfield, hasIATAcode, FAB]
Generated description
FAB is the IATA airport code for Farnborough Airport, a business aviation airport in Hampshire, England.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: FAB Target entity description: FAB is the IATA airport code for Farnborough Airport, a business aviation airport in Hampshire, England.
-
A.
FAB
FAB is the acronym for the Brazilian Air Force, the aerial warfare branch of Brazil’s armed forces.
-
B.
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.
-
C.
FAR
FAR is the acronym for Cuba’s national military organization, the Revolutionary Armed Forces.
-
D.
FAR
FAR is the commonly used abbreviation for the Intergovernmental Panel on Climate Change’s First Assessment Report, a foundational scientific evaluation of climate change published in 1990.
-
E.
FAR
FAR is the station code for Faro railway station, a key rail transport hub serving the city of Faro in southern Portugal’s Algarve region.
- 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_69b3454c772081908e20173e379e8ebe |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b351e70a748190af6f1b709a0e75e6 |
completed | March 12, 2026, 11:53 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5dbc925d881909fac944bc7ce5407 |
completed | March 14, 2026, 10:06 p.m. |
| NEDg | Description generation | batch_69b5dc578b08819095cbf6ba8470d3e0 |
completed | March 14, 2026, 10:08 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b5dd1b03508190a47bb6fb93f22ad8 |
completed | March 14, 2026, 10:11 p.m. |
Created at: March 12, 2026, 11:16 p.m.