Triple
T6138228
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gran Chaco |
E136889
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Paraguay |
E28339
|
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: Paraguay | Statement: [Gran Chaco, locatedIn, Paraguay]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Paraguay Context triple: [Gran Chaco, locatedIn, Paraguay]
-
A.
Paraguay
chosen
Paraguay is a landlocked country in central South America known for its bilingual Spanish and Guaraní culture and its location along the Paraguay and Paraná rivers.
-
B.
Argentina and Paraguay
Argentina and Paraguay are neighboring South American countries that share extensive cultural, historical, and economic ties along their common border.
-
C.
Uruguay
Uruguay is a small South American country known for its stable democracy, high standard of living, and Atlantic coastline between Brazil and Argentina.
-
D.
PARAGUAYA
PARAGUAYA is the radio callsign used by LATAM Airlines Paraguay for air traffic control and communication purposes.
-
E.
Bolivia
Bolivia is a landlocked country in central South America known for its diverse indigenous cultures, Andean and Amazonian landscapes, and administrative capitals La Paz and Sucre.
- 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_69c008a179388190a3b5a081bbf46d55 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c05c855a2481909801de9fd55686a4 |
completed | March 22, 2026, 9:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c14173d2288190920b719e221a929c |
completed | March 23, 2026, 1:34 p.m. |
Created at: March 22, 2026, 4:15 p.m.