Triple
T6779624
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nao de China route |
E155646
|
entity |
| Predicate | linksRegion |
P51005
|
FINISHED |
| Object | Americas |
E17691
|
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: Americas | Statement: [Nao de China route, linksRegion, Americas]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Americas Context triple: [Nao de China route, linksRegion, Americas]
-
A.
Americas
chosen
The Americas are the combined landmasses of North and South America, encompassing a vast region of diverse cultures, climates, and ecosystems in the Western Hemisphere.
-
B.
North America
North America is a large continent in the Northern and Western Hemispheres that includes countries such as the United States, Canada, and Mexico.
-
C.
América
América is a popular Mexican professional football club based in Mexico City, widely recognized as one of the most successful and supported teams in Liga MX.
-
D.
La América
La América was a periodical associated with José Martí that played a role in disseminating his early literary and political work, including the publication of "Ismaelillo."
-
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_69c688162bf8819088b664b5c3b5be7a |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d7ca96008190ba79563c2a9a9b0e |
completed | March 27, 2026, 7:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c71a7da01c8190995885eeb4ba6253 |
completed | March 28, 2026, 12:02 a.m. |
Created at: March 27, 2026, 2:14 p.m.