Triple
T14999451
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mitu tuberosum |
E374044
|
entity |
| Predicate | distribution |
P1356
|
FINISHED |
| Object | Ecuador |
E1141
|
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: Ecuador | Statement: [Mitu tuberosum, distribution, Ecuador]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ecuador Context triple: [Mitu tuberosum, distribution, Ecuador]
-
A.
Ecuador
chosen
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.
-
B.
Iperu
Iperu is a prominent town in Ogun State, southwestern Nigeria, known as an important commercial and cultural center of the Remo region.
-
C.
Peru
Peru is a South American country known for its rich Inca heritage, diverse landscapes from Andes mountains to Amazon rainforest, and the iconic archaeological site of Machu Picchu.
-
D.
Peru
Peru is a small rural town in Berkshire County, Massachusetts, known for its elevated terrain and quiet, forested landscape in western New England.
-
E.
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.
- 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_69d85ccc84388190aa151e5173370c8d |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded71a5618819083ae96a79735ef98 |
completed | April 15, 2026, 12:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe9dc625888190bf98eecf5f5b6707 |
completed | May 9, 2026, 2:36 a.m. |
Created at: April 10, 2026, 2:54 a.m.