Triple
T18488908
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Apure campaign |
E451763
|
entity |
| Predicate | location |
P40
|
FINISHED |
| Object | Apure |
—
|
NE NERFINISHED |
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: Apure | Statement: [Apure campaign, location, Apure]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Apure Context triple: [Apure campaign, location, Apure]
-
A.
Apure
chosen
Apure is a largely rural state in southwestern Venezuela known for its vast Llanos plains, cattle ranching, and rich river systems along the Orinoco basin.
-
B.
Guarequena
Guarequena is an alternative name for the Warekena language, an indigenous Arawakan language spoken in parts of Brazil and Venezuela.
-
C.
Jinotega
Jinotega is a city in northern Nicaragua known for its mountainous terrain and major coffee production.
-
D.
Orocué
Orocué is a small Colombian town and municipality located in the eastern plains region, known for its cattle ranching and proximity to the Meta River.
-
E.
Sácama
Sácama is a small municipality located in the Casanare Department of eastern Colombia, known for its rural character and Andean–Orinoco transitional landscapes.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8d3855d50819097fc8561b0299dd9 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e531d9d75081908e81fbccf6896bbc |
completed | April 19, 2026, 7:49 p.m. |
Created at: April 10, 2026, 11:35 a.m.