Triple
T6649596
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Exor N.V. |
E150785
|
entity |
| Predicate | ownsIndustrialAsset |
P26628
|
FINISHED |
| Object | Iveco Group N.V. |
E200926
|
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: Iveco Group N.V. | Statement: [Exor N.V., ownsIndustrialAsset, Iveco Group N.V.]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Iveco Group N.V. Context triple: [Exor N.V., ownsIndustrialAsset, Iveco Group N.V.]
-
A.
Iveco
chosen
Iveco is an Italian multinational company that designs and manufactures commercial vehicles, military vehicles, and diesel engines.
-
B.
GAZ Group
GAZ Group is a major Russian automotive manufacturer best known for producing commercial vehicles, trucks, and buses.
-
C.
Fiat Ferroviaria
Fiat Ferroviaria was an Italian railway rolling stock manufacturer known for producing trains and rail vehicles used across Europe.
-
D.
Giovanni Agnelli B.V.
Giovanni Agnelli B.V. is a Dutch holding company representing the Agnelli family’s interests and serving as the main ownership vehicle behind Exor N.V. and its industrial and financial investments.
-
E.
Renault Trucks
Renault Trucks is a French commercial vehicle manufacturer known for producing a wide range of trucks and heavy-duty vehicles for distribution, construction, and long-haul transport.
- 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_69c687f2c9508190a60b9aad31d3f358 |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6d0a3a2408190bb7be4613f896bdc |
completed | March 27, 2026, 6:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6f79642508190a2e3810e347f2e93 |
completed | March 27, 2026, 9:33 p.m. |
Created at: March 27, 2026, 2:01 p.m.