Triple
T1903662
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Scania |
E37748
|
entity |
| Predicate | hasSubsidiary |
P254
|
FINISHED |
| Object | Scania Latin America |
E37748
|
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: Scania Latin America | Statement: [Scania, hasSubsidiary, Scania Latin America]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Scania Latin America Context triple: [Scania, hasSubsidiary, Scania Latin America]
-
A.
Scania
Scania is a historical province in southern Sweden known for its fertile farmland, coastal landscapes, and former status as part of Denmark.
-
B.
Scania
chosen
Scania is a Swedish manufacturer renowned for its heavy trucks, buses, and industrial and marine engines.
-
C.
Volvo Group
Volvo Group is a Swedish multinational manufacturing company best known for producing trucks, buses, construction equipment, and marine and industrial engines.
-
D.
Iveco
Iveco is an Italian multinational company that designs and manufactures commercial vehicles, military vehicles, and diesel engines.
-
E.
Neoplan
Neoplan is a German bus and coach manufacturer renowned for its innovative, high-end touring and city buses.
- 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_69a8861be7148190a680937ec451a304 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abb1909aec8190b3259c8f969ce81e |
completed | March 7, 2026, 5:03 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae030e55f88190a9996d785066ea20 |
completed | March 8, 2026, 11:15 p.m. |
Created at: March 4, 2026, 7:35 p.m.