Triple
T21852230
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fokus |
E539531
|
entity |
| Predicate | hasAlternativeName |
P39
|
FINISHED |
| Object | FOKUS |
—
|
NE NERFINISHED |
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: FOKUS | Statement: [Fokus, hasAlternativeName, FOKUS]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: FOKUS Context triple: [Fokus, hasAlternativeName, FOKUS]
-
A.
FOKUS
chosen
FOKUS is a Fraunhofer research institute in Germany specializing in open communication systems and networked IT solutions.
-
B.
Fokus
Fokus is the Norwegian Intelligence Service’s publicly released annual assessment report on global security and threat developments.
-
C.
FOCAS
FOCAS is an optical camera and spectrograph instrument used on the Subaru Telescope for detailed imaging and spectroscopic observations of astronomical objects.
-
D.
InFocus
InFocus is an American company best known for designing and manufacturing digital projectors and other display technologies.
-
E.
FOCAL
FOCAL is an early interactive programming language developed by Digital Equipment Corporation, commonly used on PDP-series minicomputers for scientific and educational purposes.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0c47829648190bbe2d1d7033768ec |
completed | April 16, 2026, 11:14 a.m. |
| NER | Named-entity recognition | batch_69f0bd59bcbc819093829feb152e090d |
completed | April 28, 2026, 1:59 p.m. |
Created at: April 16, 2026, 6:56 p.m.