Triple
T8489349
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Iveco |
E200926
|
entity |
| Predicate | hasManufacturingFacilityIn |
P25392
|
FINISHED |
| Object | Argentina |
E5383
|
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: Argentina | Statement: [Iveco, hasManufacturingFacilityIn, Argentina]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Argentina Context triple: [Iveco, hasManufacturingFacilityIn, Argentina]
-
A.
Argentina
chosen
Argentina is a large South American nation known for its diverse landscapes from the Andes to the Pampas, its vibrant culture including tango and football, and its capital city Buenos Aires.
-
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.
Argentina and Chile
Argentina and Chile are neighboring South American countries that share a long Andean border, diverse climates and landscapes, and deep historical, cultural, and economic ties.
-
D.
Argentina and Bolivia
Argentina and Bolivia are neighboring South American countries that share a long Andean and lowland frontier, including sections defined by the Bermejo River.
-
E.
Argen
Argen is a river in southern Germany that flows through the Allgäu region before emptying into Lake Constance.
- 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_69ca831d7b148190a6e32c1de43ab13b |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe5581d308190b47d76dd49a36529 |
completed | March 31, 2026, 3:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce88bc6b008190a93fbd41089d6c72 |
completed | April 2, 2026, 3:18 p.m. |
Created at: March 30, 2026, 6:13 p.m.