Triple
T15227949
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Winona Municipal Airport – Max Conrad Field |
E363924
|
entity |
| Predicate | IATAcode |
P418
|
FINISHED |
| Object |
ONA
ONA is the IATA airport code for Winona Municipal Airport – Max Conrad Field, a public airport serving Winona, Minnesota.
|
E1145253
|
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: ONA | Statement: [Winona Municipal Airport – Max Conrad Field, IATAcode, ONA]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: ONA Context triple: [Winona Municipal Airport – Max Conrad Field, IATAcode, ONA]
-
A.
ON
ON is the stock ticker symbol for onsemi, a leading semiconductor manufacturer specializing in power and sensing technologies.
-
B.
OUN
OUN is the National Weather Service forecast office identifier for the Norman, Oklahoma weather forecast and warning center.
-
C.
ANO
ANO is the ICAO airline designator assigned to Airnorth, a regional airline based in Australia.
-
D.
ONS
ONS is the United Kingdom’s largest independent producer of official statistics and its recognized national statistical institute.
-
E.
ONS
ONS is the abbreviation for the Object Naming Service, a system used to assign and resolve unique identifiers for objects in distributed computing environments.
- 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: ONA Triple: [Winona Municipal Airport – Max Conrad Field, IATAcode, ONA]
Generated description
ONA is the IATA airport code for Winona Municipal Airport – Max Conrad Field, a public airport serving Winona, Minnesota.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: ONA Target entity description: ONA is the IATA airport code for Winona Municipal Airport – Max Conrad Field, a public airport serving Winona, Minnesota.
-
A.
ON
ON is the stock ticker symbol for onsemi, a leading semiconductor manufacturer specializing in power and sensing technologies.
-
B.
OUN
OUN is the National Weather Service forecast office identifier for the Norman, Oklahoma weather forecast and warning center.
-
C.
ANO
ANO is the ICAO airline designator assigned to Airnorth, a regional airline based in Australia.
-
D.
ONS
ONS is the United Kingdom’s largest independent producer of official statistics and its recognized national statistical institute.
-
E.
ONS
ONS is the abbreviation for the Object Naming Service, a system used to assign and resolve unique identifiers for objects in distributed computing environments.
- 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_69d85a0ce24c81909c4d3b6475548c95 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e0078ccdf48190b34eabd9e24e45a1 |
completed | April 15, 2026, 9:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fedd379ac081909ebb3a18c2ee3b3c |
completed | May 9, 2026, 7:07 a.m. |
| NEDg | Description generation | batch_69fedf0fd0208190a24dee813fd5e2e9 |
completed | May 9, 2026, 7:15 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fee01d74a48190a9134f9e238dc27e |
completed | May 9, 2026, 7:19 a.m. |
Created at: April 10, 2026, 3:12 a.m.