Triple
T14874873
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Buzzr |
E349839
|
entity |
| Predicate | hasStreamingPlatform |
P3189
|
FINISHED |
| Object | Freevee |
E358599
|
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: Freevee | Statement: [Buzzr, hasStreamingPlatform, Freevee]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Freevee Context triple: [Buzzr, hasStreamingPlatform, Freevee]
-
A.
Viki
Viki is the commonly used nickname for Viki Weisskopf, likely referring to her in informal or personal contexts.
-
B.
Zeebo
Zeebo was a Brazil-focused, low-cost 3G-enabled video game console designed to bring digital gaming to emerging markets.
-
C.
Viddy
Viddy was a mobile social video-sharing app that allowed users to create, edit, and share short video clips with an online community.
-
D.
Xumo
chosen
Xumo is a free, ad-supported streaming television service offering a variety of live and on-demand channels and content.
-
E.
Fivelingo
Fivelingo is a historical region in the province of Groningen in the Netherlands, known for its medieval origins and former administrative significance.
- 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_69d822ee4f408190b6ac3b2fa434f0df |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69ded5e3e5d48190a132f2cf012b01e2 |
completed | April 15, 2026, 12:03 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe6b52c12481908d0173a2a3ed854b |
completed | May 8, 2026, 11:01 p.m. |
Created at: April 10, 2026, 1:55 a.m.