Triple
T21993399
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Goede tijden, slechte tijden |
E543144
|
entity |
| Predicate | originalNetwork |
P2594
|
FINISHED |
| Object | RTL 4 |
—
|
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: RTL 4 | Statement: [Goede tijden, slechte tijden, originalNetwork, RTL 4]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: RTL 4 Context triple: [Goede tijden, slechte tijden, originalNetwork, RTL 4]
-
A.
RTL 4
chosen
RTL 4 is a major Dutch commercial television channel known for broadcasting popular entertainment shows, dramas, and reality series.
-
B.
RTL 5
RTL 5 is a Dutch commercial television channel known for broadcasting entertainment, reality shows, and imported series, and is part of the RTL Nederland network.
-
C.
R4
R4 is a government office building in Oslo that forms part of Norway’s central Regjeringskvartalet complex.
-
D.
R4
R4 is a commuter rail line in the Rodalies de Catalunya network serving key suburban and regional routes in Catalonia, Spain.
-
E.
R4
R4 is the common shorthand for the Renault 4, a popular small economy car produced by the French manufacturer Renault from the early 1960s through the early 1990s.
- 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_69e11e2c814c8190837d072789000486 |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f1270f77fc8190aadcc02760d65ac0 |
completed | April 28, 2026, 9:30 p.m. |
Created at: April 16, 2026, 8:17 p.m.