Triple
T34181173
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Göran Rosenberg |
E876820
|
entity |
| Predicate | hasOccupationInCountry |
P131264
|
FINISHED |
| Object | journalist in Sweden |
—
|
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: journalist in Sweden | Statement: [Göran Rosenberg, hasOccupationInCountry, journalist in Sweden]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasOccupationInCountry Context triple: [Göran Rosenberg, hasOccupationInCountry, journalist in Sweden]
-
A.
hasOccupationInWork
Indicates that an entity holds or performs a specific occupation within a particular work, project, or creative production.
-
B.
worksInCountry
chosen
Indicates that an entity performs its work or professional activities within the specified country.
-
C.
workFromCountry
Indicates that an entity performs their work or job while being physically located in a specified country.
-
D.
hasOccupationInReality
Indicates that an entity holds or performs a specific occupation in the real world, as opposed to fictional or hypothetical contexts.
-
E.
endedOccupationOf
Indicates that one entity brought another entity’s occupation or control of a place or position to an end.
- 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_69f349ae640c8190b9cd220b5368d8b6 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69f71362f1448190985a80ce7af475cb |
completed | May 3, 2026, 9:20 a.m. |
| PD | Predicate disambiguation | batch_69f7127884388190884f23d181a65d19 |
completed | May 3, 2026, 9:16 a.m. |
Created at: May 1, 2026, 1:54 a.m.