Triple
T24633023
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Exor N.V. |
E609728
|
entity |
| Predicate | hasInvestmentInSector |
P116570
|
FINISHED |
| Object | automotive |
—
|
LITERAL 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: automotive | Statement: [Exor N.V., hasInvestmentInSector, automotive]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasInvestmentInSector Context triple: [Exor N.V., hasInvestmentInSector, automotive]
-
A.
hasMarketSector
chosen
Indicates that an entity operates within, is associated with, or belongs to a particular market sector or industry segment.
-
B.
hasInvestmentTheme
Indicates that an investment, fund, or financial product is associated with a particular overarching theme or strategic focus (such as technology, sustainability, or healthcare).
-
C.
investsIn
Indicates that one entity allocates resources, typically money or capital, into another entity with the expectation of future returns or benefits.
-
D.
hasGICSsector
Indicates that an entity is classified as belonging to a particular Global Industry Classification Standard (GICS) sector.
-
E.
hasOccupationSector
Indicates that an entity’s occupation belongs to or is categorized within a particular economic or professional sector.
- F. None of above.
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_69e2c4d28f848190ac38c400060e943d |
completed | April 17, 2026, 11:40 p.m. |
| NER | Named-entity recognition | batch_69f2be064ff88190b5d9e5ec75a41242 |
completed | April 30, 2026, 2:27 a.m. |
| PD | Predicate disambiguation | batch_69f2a6d0ab708190b2e3b94dd20ca76b |
completed | April 30, 2026, 12:48 a.m. |
Created at: April 18, 2026, 2:32 a.m.