Triple
T35615540
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | remuneration committee of Stellantis |
E1029153
|
entity |
| Predicate | typeOfRemunerationCovered |
P14176
|
FINISHED |
| Object | fixed salary |
—
|
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: fixed salary | Statement: [remuneration committee of Stellantis, typeOfRemunerationCovered, fixed salary]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typeOfRemunerationCovered Context triple: [remuneration committee of Stellantis, typeOfRemunerationCovered, fixed salary]
-
A.
compensationCategory
chosen
Indicates the type or classification of compensation associated with an entity, such as how or in what form payment or remuneration is provided.
-
B.
salaryType
Indicates the classification or structure of compensation associated with an entity, such as whether pay is salaried, hourly, commission-based, or another type.
-
C.
typeOfMinimumWage
Indicates the specific category or kind of minimum wage that applies in a given context (e.g., by sector, worker type, or legal framework).
-
D.
earnerType
Indicates the category or role that characterizes how an entity earns income or compensation.
-
E.
compensationIncludes
Indicates that a specified form of payment or benefit is part of the overall compensation provided in a given context.
- 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_69f76e0709408190bbe322bf1707ef6b |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_6a01bb9c33cc8190a95ba124d97edf99 |
completed | May 11, 2026, 11:21 a.m. |
| PD | Predicate disambiguation | batch_6a01bb6772148190a243d1141ad0a237 |
completed | May 11, 2026, 11:20 a.m. |
Created at: May 3, 2026, 4:05 p.m.