Triple
T2286002
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Waterloo Regional Airport |
E51392
|
entity |
| Predicate | pushpinMap |
P1987
|
FINISHED |
| Object | USA |
E14
|
NE FINISHED |
How this triple was built (2 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: USA | Statement: [Waterloo Regional Airport, pushpinMap, USA]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: USA Context triple: [Waterloo Regional Airport, pushpinMap, USA]
-
A.
US
US is the IATA airline designator code assigned to the former American airline US Airways.
-
B.
EUA
EUA (European University Association) is a major organization representing and supporting higher education institutions and national rectors’ conferences across Europe in areas such as policy, quality assurance, and institutional development.
-
C.
United States of America
chosen
The United States of America is a large federal republic in North America known for its global political, economic, military, and cultural influence.
-
D.
America
America is the landmass in the Western Hemisphere comprising the continents of North and South America, widely recognized for its vast geographic, cultural, and political diversity.
-
E.
Amerika
Amerika is a novel by Franz Kafka that follows a young European immigrant’s surreal and often absurd experiences in the United States.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69a88b08e4308190bdac9aebcca1c91a |
completed | March 4, 2026, 7:42 p.m. |
| NER | Named-entity recognition | batch_69abc24730208190af8a5cf443d334f7 |
completed | March 7, 2026, 6:14 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69aeb3a390bc8190891ebd5d8a48d818 |
completed | March 9, 2026, 11:48 a.m. |
Created at: March 4, 2026, 7:48 p.m.