Triple
T15731109
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Charlottesville–Albemarle Airport |
E381346
|
entity |
| Predicate | IATAcode |
P418
|
FINISHED |
| Object |
CHO
CHO is the three-letter IATA airport code for Charlottesville–Albemarle Airport in Virginia, United States.
|
E1174118
|
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: CHO | Statement: [Charlottesville–Albemarle Airport, IATAcode, CHO]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: CHO Context triple: [Charlottesville–Albemarle Airport, IATAcode, CHO]
-
A.
CHE
CHE is the acronym for the South Carolina Commission on Higher Education, the state agency responsible for coordinating and overseeing public higher education in South Carolina.
-
B.
CHE
CHE is the three-letter ISO 3166-1 alpha-3 country code for Switzerland.
-
C.
CH
CH is the post-nominal abbreviation for Companion of Honour, a prestigious British award recognizing outstanding achievements in fields such as the arts, literature, music, science, politics, industry, or religion.
-
D.
CH
CH is the ISO 3166-1 alpha-2 country code for Switzerland.
-
E.
CH
CH is the common abbreviation for Christ’s Hospital, a historic English independent boarding school founded in the 16th century and known for its distinctive bluecoat uniform and charitable educational mission.
- 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: CHO Triple: [Charlottesville–Albemarle Airport, IATAcode, CHO]
Generated description
CHO is the three-letter IATA airport code for Charlottesville–Albemarle Airport in Virginia, United States.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: CHO Target entity description: CHO is the three-letter IATA airport code for Charlottesville–Albemarle Airport in Virginia, United States.
-
A.
CHE
CHE is the acronym for the South Carolina Commission on Higher Education, the state agency responsible for coordinating and overseeing public higher education in South Carolina.
-
B.
CHE
CHE is the three-letter ISO 3166-1 alpha-3 country code for Switzerland.
-
C.
CH
CH is the post-nominal abbreviation for Companion of Honour, a prestigious British award recognizing outstanding achievements in fields such as the arts, literature, music, science, politics, industry, or religion.
-
D.
CH
CH is the ISO 3166-1 alpha-2 country code for Switzerland.
-
E.
CH
CH is the postcode area covering Chester and its surrounding region in northwest England.
- 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_69d86d9cdb648190bf3171be0bd7d872 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e04fb61cb881908b158609c1ccfa1e |
completed | April 16, 2026, 2:55 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff82fcb4e4819097bd0591bbcc3b71 |
completed | May 9, 2026, 6:54 p.m. |
| NEDg | Description generation | batch_69ff83ca33d08190816130bf2ea735df |
completed | May 9, 2026, 6:58 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ff846436e48190b711da134c9a3b81 |
completed | May 9, 2026, 7 p.m. |
Created at: April 10, 2026, 4:46 a.m.