Triple
T15682803
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dassault Mirage 5 |
E377619
|
entity |
| Predicate | exportedTo |
P5775
|
FINISHED |
| Object | Colombia |
E12035
|
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: Colombia | Statement: [Dassault Mirage 5, exportedTo, Colombia]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Colombia Context triple: [Dassault Mirage 5, exportedTo, Colombia]
-
A.
Colombia
Colombia is a station on Madrid's Metro network, serving Line 8 and acting as an important interchange point in the city's public transportation system.
-
B.
Colombia
chosen
Colombia is a transcontinental country in northern South America, known for its diverse landscapes from Andes mountains to Amazon rainforest, rich cultural heritage, and major cities like Bogotá and Medellín.
-
C.
Chinchiná
Chinchiná is a Colombian town and municipality known for its coffee production and location in the central Andean region.
-
D.
Ecuador
Ecuador is a South American country on the Pacific coast, known for its diverse geography that includes part of the Amazon rainforest, the Andean highlands, and the Galápagos Islands.
-
E.
Venezuela
Venezuela is a South American country known for its vast oil reserves, diverse landscapes ranging from Caribbean coastlines to Andean mountains and Amazon rainforest, and its Spanish-speaking population.
- 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_69d85cd2e28481909d4e975bee20872f |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04f31b5b881908e46ecd9fc6048ab |
completed | April 16, 2026, 2:53 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff6ee4c8688190ae2fefb56171161a |
completed | May 9, 2026, 5:29 p.m. |
Created at: April 10, 2026, 4:16 a.m.