Triple
T8269997
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bell Media |
E193402
|
entity |
| Predicate | owns |
P347
|
FINISHED |
| Object | RDS |
E37965
|
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: RDS | Statement: [Bell Media, owns, RDS]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: RDS Context triple: [Bell Media, owns, RDS]
-
A.
RDS
chosen
RDS is a Canadian French-language sports television network that broadcasts a wide range of professional and amateur sporting events.
-
B.
RDS2
RDS2 is a Swiss French-language television channel that serves as a secondary sports-focused outlet to the main Réseau des sports (RDS) network.
-
C.
Amazon RDS
Amazon RDS is a managed relational database service by Amazon Web Services that simplifies setup, operation, and scaling of databases in the cloud.
-
D.
ApsaraDB for RDS
ApsaraDB for RDS is Alibaba Cloud’s managed relational database service that provides scalable, high-availability SQL databases with automated management and security features.
-
E.
DB
DB is the commonly used abbreviation for Deutsche Bahn, Germany’s national railway company and one of the largest rail operators in Europe.
- 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_69ca82e14ae481908ffdb822cd2192bc |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb795243fc8190a66afef7476e1147 |
completed | March 31, 2026, 7:35 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cd683d04e081908b0ce81e866f0311 |
completed | April 1, 2026, 6:47 p.m. |
Created at: March 30, 2026, 5:50 p.m.