Triple
T4433470
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Frankfurt Airport |
E95589
|
entity |
| Predicate | IATAcode |
P418
|
FINISHED |
| Object |
FRA
FRA is the three-letter IATA airport code for Frankfurt Airport, one of Europe’s busiest international aviation hubs located in Frankfurt, Germany.
|
E440597
|
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: FRA | Statement: [Frankfurt Airport, IATAcode, FRA]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: FRA Context triple: [Frankfurt Airport, IATAcode, FRA]
-
A.
FRA
FRA is the standard abbreviation used to refer to the Royal Moroccan Air Force, the aerial warfare branch of Morocco’s armed forces.
-
B.
FRA
FRA is the acronym for the Global Forest Resources Assessment, a periodic FAO-led study that evaluates the state and trends of the world’s forests.
-
C.
FRA
FRA is the three-letter ISO 3166-1 alpha-3 country code that uniquely identifies France in international standards and data systems.
-
D.
FRA
FRA is the United States government agency responsible for regulating and overseeing the nation’s railroad safety, infrastructure, and operations.
-
E.
FRA 2000
FRA 2000 is a landmark edition of the FAO’s Global Forest Resources Assessment that provided a comprehensive global overview of forest extent, condition, and management at the turn of the millennium.
- 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: FRA Triple: [Frankfurt Airport, IATAcode, FRA]
Generated description
FRA is the three-letter IATA airport code for Frankfurt Airport, one of Europe’s busiest international aviation hubs located in Frankfurt, Germany.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: FRA Target entity description: FRA is the three-letter IATA airport code for Frankfurt Airport, one of Europe’s busiest international aviation hubs located in Frankfurt, Germany.
-
A.
FRA
FRA is the three-letter ISO 3166-1 alpha-3 country code that uniquely identifies France in international standards and data systems.
-
B.
FRA
FRA is the United States government agency responsible for regulating and overseeing the nation’s railroad safety, infrastructure, and operations.
-
C.
FRA
FRA is the standard abbreviation used to refer to the Royal Moroccan Air Force, the aerial warfare branch of Morocco’s armed forces.
-
D.
FRA
FRA is the acronym for the Global Forest Resources Assessment, a periodic FAO-led study that evaluates the state and trends of the world’s forests.
-
E.
FRA 2000
FRA 2000 is a landmark edition of the FAO’s Global Forest Resources Assessment that provided a comprehensive global overview of forest extent, condition, and management at the turn of the millennium.
- 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_69b3453ea2b48190a26f154b3b8fece5 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b35587bc048190aee8e0ed94b6e064 |
completed | March 13, 2026, 12:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b6137171148190b77a6f783d5cf315 |
completed | March 15, 2026, 2:03 a.m. |
| NEDg | Description generation | batch_69b6177d1a588190991fddf506239d22 |
completed | March 15, 2026, 2:20 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b61b6bc1448190b30d444a821bb1a5 |
completed | March 15, 2026, 2:37 a.m. |
Created at: March 12, 2026, 11:31 p.m.