Triple
T12825389
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Alfonso Bonilla Aragón International Airport |
E306637
|
entity |
| Predicate | IATACode |
P418
|
FINISHED |
| Object |
CLO
CLO is the IATA airport code for Alfonso Bonilla Aragón International Airport, the main airport serving Cali, Colombia.
|
E1004459
|
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: CLO | Statement: [Alfonso Bonilla Aragón International Airport, IATACode, CLO]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: CLO Context triple: [Alfonso Bonilla Aragón International Airport, IATACode, CLO]
-
A.
CLO
CLO is the acronym for the Conselh de la Lenga Occitana, the official body responsible for regulating and standardizing the Occitan language.
-
B.
Tailo
Tailo is a widely used Latin-based romanization system for writing Taiwanese Hokkien, employed in education, literature, and language preservation.
-
C.
COS
COS is the College of Science at Northeastern University, encompassing disciplines such as biology, chemistry, physics, mathematics, and related scientific fields.
-
D.
COS
COS is the French Armed Forces' elite joint command responsible for planning and conducting special operations.
-
E.
COS
COS is a Hubble Space Telescope instrument designed to study the origins and evolution of the universe by analyzing the ultraviolet light from distant astronomical objects.
- 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: CLO Triple: [Alfonso Bonilla Aragón International Airport, IATACode, CLO]
Generated description
CLO is the IATA airport code for Alfonso Bonilla Aragón International Airport, the main airport serving Cali, Colombia.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: CLO Target entity description: CLO is the IATA airport code for Alfonso Bonilla Aragón International Airport, the main airport serving Cali, Colombia.
-
A.
CLO
CLO is the acronym for the Conselh de la Lenga Occitana, the official body responsible for regulating and standardizing the Occitan language.
-
B.
Tailo
Tailo is a widely used Latin-based romanization system for writing Taiwanese Hokkien, employed in education, literature, and language preservation.
-
C.
COS
COS is the College of Science at Northeastern University, encompassing disciplines such as biology, chemistry, physics, mathematics, and related scientific fields.
-
D.
COS
COS is the French Armed Forces' elite joint command responsible for planning and conducting special operations.
-
E.
COS
COS is a Hubble Space Telescope instrument designed to study the origins and evolution of the universe by analyzing the ultraviolet light from distant astronomical objects.
- 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_69d7bdf46c448190b1faa55aaacb6317 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d96facb2d48190bc12efc00c9da360 |
completed | April 10, 2026, 9:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f68ed4f7388190ba989b8a79bd7c6d |
completed | May 2, 2026, 11:55 p.m. |
| NEDg | Description generation | batch_69f691341d0081909ca3b281ee64b42b |
completed | May 3, 2026, 12:05 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f692361c3c81909078a19be1a86231 |
completed | May 3, 2026, 12:09 a.m. |
Created at: April 9, 2026, 5:32 p.m.