Triple
T13780875
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | DR |
E331126
|
entity |
| Predicate | hasOnlineService |
P57
|
FINISHED |
| Object | dr.dk |
E331126
|
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: dr.dk | Statement: [DR, hasOnlineService, dr.dk]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: dr.dk Context triple: [DR, hasOnlineService, dr.dk]
-
A.
DANSKE
DANSKE is the stock ticker symbol for Danske Bank, a major Nordic financial services group headquartered in Denmark.
-
B.
DR
chosen
DR is Denmark’s national public service broadcasting organization, operating television, radio, and online media.
-
C.
DR
DR was the abbreviation for the Deutsche Reichsbahn, the state railway company of Germany (and later East Germany) that operated much of the country’s rail transport during the 20th century.
-
D.
Danish Days
Danish Days is an annual cultural festival in Solvang, California that celebrates the town’s Danish heritage with traditional food, music, dancing, and parades.
-
E.
Banedanmark
Banedanmark is the Danish government agency responsible for owning, maintaining, and managing most of Denmark’s railway infrastructure.
- 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_69d81c583b0081909e408a17db517a21 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de02460a688190a27874f8d35819c7 |
completed | April 14, 2026, 9 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7b079013881908e9f5412e5dfb0b2 |
completed | May 3, 2026, 8:30 p.m. |
Created at: April 9, 2026, 10:11 p.m.