Triple
T10209842
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | RTL Group |
E242297
|
entity |
| Predicate | subsidiary |
P258
|
FINISHED |
| Object | RTL Hungary |
E849558
|
NE 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: RTL Hungary | Statement: [RTL Group, subsidiary, RTL Hungary]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: RTL Hungary Context triple: [RTL Group, subsidiary, RTL Hungary]
-
A.
RTL Hungary
chosen
RTL Hungary is a major Hungarian commercial television broadcaster and media company known for its popular entertainment, news, and reality programming.
-
B.
NI Hungary
NI Hungary is the Hungarian branch of National Instruments, focusing on developing and supporting test, measurement, and automation solutions.
-
C.
Eastern Hungary
Eastern Hungary is a geographic region of Hungary that includes major cities such as Debrecen and is known for its plains, cultural heritage, and agricultural significance.
-
D.
Ungar
Ungar is a surname of Germanic and Central European origin, historically associated with people from Hungary or of Hungarian descent.
-
E.
Northern Hungary
Northern Hungary is a region of Hungary known for its industrial cities like Miskolc, historic castles, and the Bükk and Mátra mountain ranges.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d381ae26c48190985abd0e25ee5d04 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d395fbed008190b66996f5bb397853 |
completed | April 6, 2026, 11:16 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d6a7f6730081908b941eaeb6c00993 |
completed | April 8, 2026, 7:09 p.m. |
Created at: April 6, 2026, 11 a.m.