Triple
T30726682
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | NXPI |
E782295
|
entity |
| Predicate | underlyingCompanyFocusArea |
P6749
|
FINISHED |
| Object | automotive semiconductors |
—
|
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 semiconductors | Statement: [NXPI, underlyingCompanyFocusArea, automotive semiconductors]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: underlyingCompanyFocusArea Context triple: [NXPI, underlyingCompanyFocusArea, automotive semiconductors]
-
A.
underlyingCompanyBusinessFocus
chosen
Indicates the primary industry, sector, or type of business activity that the underlying company is focused on.
-
B.
acquiredCompanyFocus
Indicates that a company’s primary business focus or specialization changed as a result of acquiring another company.
-
C.
hasUnderlyingCompanyTherapeuticArea
Indicates that a company is associated with or focused on a particular therapeutic area in its activities or offerings.
-
D.
underlyingCompanyBusinessDescription
Indicates the detailed description of the primary business activities or operations conducted by the underlying company.
-
E.
industryOfUnderlyingCompany
Indicates the industry sector in which the underlying company associated with this entity operates.
- 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_69f224ad9f9c81908e02a79ae0001137 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f7a225a77c81908f8953ccfeb14336 |
completed | May 3, 2026, 7:29 p.m. |
| PD | Predicate disambiguation | batch_69f7a06d4f108190bae3ab9ae431d2c7 |
completed | May 3, 2026, 7:22 p.m. |
Created at: April 29, 2026, 8:37 p.m.