Triple
T7283154
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Congo Airways |
E163798
|
entity |
| Predicate | currencyUsedForTickets |
P245
|
FINISHED |
| Object | US dollar |
E105
|
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: US dollar | Statement: [Congo Airways, currencyUsedForTickets, US dollar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: US dollar Context triple: [Congo Airways, currencyUsedForTickets, US dollar]
-
A.
US dollar
chosen
The US dollar is the official currency of the United States and the world’s primary reserve currency used widely in global trade and finance.
-
B.
Dollar
Dollar was a British pop duo, formed by David Van Day and Thereza Bazar, known for their catchy synth-pop hits in the late 1970s and early 1980s.
-
C.
Dollar
Dollar is a small historic town in Clackmannanshire, Scotland, known for its scenic setting near the Ochil Hills and the nearby Castle Campbell.
-
D.
USD
USD (Universal Scene Description) is an open-source 3D scene description and interchange framework developed by Pixar, widely used for creating, composing, and collaborating on complex virtual worlds and assets.
-
E.
USD
USD is a public research university located in Vermillion, South Dakota, known for its programs in law, medicine, and business.
- 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_69c6886093b88190a254b1ce6db8bae7 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6f041f4a88190b85cff1ee5f9a6d0 |
completed | March 27, 2026, 9:01 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7db3ae6a08190820c7096cbfea521 |
completed | March 28, 2026, 1:44 p.m. |
Created at: March 27, 2026, 2:59 p.m.