Triple
T10193009
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | La Isabela International Airport |
E238084
|
entity |
| Predicate | operator |
P179
|
FINISHED |
| Object |
Aerodom
Aerodom is a Dominican airport management company that operates several of the country’s main airports under concession agreements.
|
E847295
|
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: Aerodom | Statement: [La Isabela International Airport, operator, Aerodom]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Aerodom Context triple: [La Isabela International Airport, operator, Aerodom]
-
A.
Aerograd
Aerograd is a 1935 Soviet science fiction and propaganda film directed by Alexander Dovzhenko, set in a futuristic Far Eastern border town threatened by foreign and internal enemies.
-
B.
Avion
Avion is a commune in the Pas-de-Calais department in northern France.
-
C.
Ramport Aero
Ramport Aero is the company responsible for managing and operating Zhukovsky International Airport near Moscow, Russia.
-
D.
Equair
Equair is an Ecuadorian airline that operated domestic passenger flights, notably serving routes from Guayaquil and Quito.
-
E.
Aero
Aero is a high-performance, sport-oriented trim level used by Saab for its 9-3 and other models, typically featuring more powerful engines and upgraded equipment.
- 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: Aerodom Triple: [La Isabela International Airport, operator, Aerodom]
Generated description
Aerodom is a Dominican airport management company that operates several of the country’s main airports under concession agreements.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Aerodom Target entity description: Aerodom is a Dominican airport management company that operates several of the country’s main airports under concession agreements.
-
A.
Aerograd
Aerograd is a 1935 Soviet science fiction and propaganda film directed by Alexander Dovzhenko, set in a futuristic Far Eastern border town threatened by foreign and internal enemies.
-
B.
Avion
Avion is a commune in the Pas-de-Calais department in northern France.
-
C.
Ramport Aero
Ramport Aero is the company responsible for managing and operating Zhukovsky International Airport near Moscow, Russia.
-
D.
Equair
Equair is an Ecuadorian airline that operated domestic passenger flights, notably serving routes from Guayaquil and Quito.
-
E.
Aero
Aero is a high-performance, sport-oriented trim level used by Saab for its 9-3 and other models, typically featuring more powerful engines and upgraded equipment.
- 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_69ca84de1b208190bf17bb305b002605 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdedc675008190b8248325f5a208bf |
completed | April 2, 2026, 4:17 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d317ca2cf481909cf715ef9248be3c |
completed | April 6, 2026, 2:17 a.m. |
| NEDg | Description generation | batch_69d3188886908190ba0a5539ce942980 |
completed | April 6, 2026, 2:20 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d31c4fb8288190bbc6b3d4a79dafb1 |
completed | April 6, 2026, 2:37 a.m. |
Created at: March 30, 2026, 9:13 p.m.