Triple
T15500108
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Guahibo |
E378929
|
entity |
| Predicate | region |
P40
|
FINISHED |
| Object | Llanos |
E1127984
|
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: Llanos | Statement: [Guahibo, region, Llanos]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Llanos Context triple: [Guahibo, region, Llanos]
-
A.
Los Llanos
chosen
Los Llanos is a vast tropical grassland plain in northern South America, known for its cattle ranching, rich wildlife, and seasonal flooding.
-
B.
Llanos de Moxos
Llanos de Moxos is a vast seasonally flooded tropical savanna and wetland region in northern Bolivia, known for its rich biodiversity and extensive pre-Columbian earthworks.
-
C.
Orinoco Llanos floodplains
The Orinoco Llanos floodplains are vast seasonally inundated grasslands in the Orinoco River basin of Venezuela and Colombia, known for their rich biodiversity and extensive wetlands.
-
D.
La Sabana
La Sabana is a locality within the coastal region of Acapulco in the Mexican state of Guerrero, known as part of the broader urban and suburban area surrounding the resort city.
-
E.
Cuban plains
The Cuban plains are broad, low-lying fertile regions of Cuba characterized by extensive agriculture and relatively flat terrain.
- 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_69d85cd53a7c819080f5b9042c4c199e |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e03fcb4e8c81908e4ab463e3ae252b |
completed | April 16, 2026, 1:47 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff3667a53c81908be789f99e580265 |
completed | May 9, 2026, 1:28 p.m. |
Created at: April 10, 2026, 3:54 a.m.