Triple
T31036452
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Appuntato Scelto |
E790864
|
entity |
| Predicate | hasCareerFunction |
P196626
|
FINISHED |
| Object | reward for experience and service length |
—
|
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: reward for experience and service length | Statement: [Appuntato Scelto, hasCareerFunction, reward for experience and service length]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCareerFunction Context triple: [Appuntato Scelto, hasCareerFunction, reward for experience and service length]
-
A.
hasCareerService
Indicates that an entity provides or is associated with a career-related support or advisory service for another entity.
-
B.
hasCareerScope
Indicates that an entity’s career, role, or profession extends over, is relevant to, or is defined within a particular domain, field, or scope.
-
C.
hasCareerTrack
Indicates that an entity is associated with or follows a particular career path or professional progression.
-
D.
hasCareerStage
Indicates the specific phase or stage of a person's or entity's professional or occupational progression.
-
E.
hasIndustryRole
Indicates that an entity holds or performs a specific role, function, or position within a particular industry or sector.
- F. None of above. chosen
Provenance (4 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_69f224c97a788190b5da1ead6038a74e |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69fe5ec9028081909ae3d6fbe2f4cbbc |
completed | May 8, 2026, 10:08 p.m. |
| PD | Predicate disambiguation | batch_69fe5e1d715881909fc516fafc707644 |
completed | May 8, 2026, 10:05 p.m. |
| PDg | Predicate description generation | batch_69fe5ec84910819094cb15269ace7c51 |
completed | May 8, 2026, 10:08 p.m. |
Created at: April 29, 2026, 8:59 p.m.