Triple
T15463842
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rick Husband Amarillo International Airport |
E371972
|
entity |
| Predicate | IATAcode |
P418
|
FINISHED |
| Object |
AMA
AMA is the three-letter IATA airport code for Rick Husband Amarillo International Airport in Amarillo, Texas.
|
E1159005
|
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: AMA | Statement: [Rick Husband Amarillo International Airport, IATAcode, AMA]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: AMA Context triple: [Rick Husband Amarillo International Airport, IATAcode, AMA]
-
A.
AMA
AMA is the leading professional association and lobbying group representing physicians and medical students in the United States.
-
B.
AMA
AMA is the commonly used abbreviation for Japan’s Antimonopoly Act, the core law regulating competition and prohibiting monopolistic practices in the country.
-
C.
AMI
AMI is the abbreviation commonly used for the Italian Air Force, the air and space warfare branch of Italy’s armed forces.
-
D.
AMI
AMI is the station code for Amityville station, a Long Island Rail Road commuter rail stop in Amityville, New York.
-
E.
ARAM
ARAM is a fast-paced League of Legends game mode where teams fight continuously on a single narrow bridge-like map with randomly assigned champions.
- 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: AMA Triple: [Rick Husband Amarillo International Airport, IATAcode, AMA]
Generated description
AMA is the three-letter IATA airport code for Rick Husband Amarillo International Airport in Amarillo, Texas.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: AMA Target entity description: AMA is the three-letter IATA airport code for Rick Husband Amarillo International Airport in Amarillo, Texas.
-
A.
AMA
AMA is the leading professional association and lobbying group representing physicians and medical students in the United States.
-
B.
AMA
AMA is the commonly used abbreviation for Japan’s Antimonopoly Act, the core law regulating competition and prohibiting monopolistic practices in the country.
-
C.
AMI
AMI is the station code for Amityville station, a Long Island Rail Road commuter rail stop in Amityville, New York.
-
D.
AMI
AMI is the abbreviation commonly used for the Italian Air Force, the air and space warfare branch of Italy’s armed forces.
-
E.
ARAM
ARAM is a fast-paced League of Legends game mode where teams fight continuously on a single narrow bridge-like map with randomly assigned champions.
- 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_69d85cc8bd308190886949510b42e764 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e03f1927708190a0d2b63e75469a0e |
completed | April 16, 2026, 1:44 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff2cff942081908a2f5351079666a3 |
completed | May 9, 2026, 12:47 p.m. |
| NEDg | Description generation | batch_69ff2ed38a8c8190af82acf2a90a1433 |
completed | May 9, 2026, 12:55 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ff2f3ab6988190b4cefe2f55c4101c |
completed | May 9, 2026, 12:57 p.m. |
Created at: April 10, 2026, 3:33 a.m.