Triple
T6360651
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | VK |
E143098
|
entity |
| Predicate | operates |
P24
|
FINISHED |
| Object |
VK Clips
VK Clips is a short-form video platform integrated into the Russian social network VK, allowing users to create, share, and watch vertical video content.
|
E587898
|
NE FINISHED |
How this triple was built (4 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: VK Clips | Statement: [VK, operates, VK Clips]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: VK Clips Context triple: [VK, operates, VK Clips]
-
A.
VIDO
VIDO is a Canadian research organization at the University of Saskatchewan specializing in vaccine development and infectious disease research for both humans and animals.
-
B.
Vdio
Vdio was an online video-on-demand and streaming service launched by Skype and Rdio co-founder Janus Friis as an attempt to compete with platforms like Netflix.
-
C.
VIDN
VIDN is the four-letter ICAO airport code assigned to Noida International Airport in Uttar Pradesh, India.
-
D.
YouTube Shorts
YouTube Shorts is YouTube’s short-form vertical video platform designed for quick, snackable content similar to TikTok and Instagram Reels.
-
E.
Shorts
Shorts is a surname shared by various individuals, including American football player Cecil Shorts III.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: VK Clips Triple: [VK, operates, VK Clips]
Generated description
VK Clips is a short-form video platform integrated into the Russian social network VK, allowing users to create, share, and watch vertical video content.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: VK Clips Target entity description: VK Clips is a short-form video platform integrated into the Russian social network VK, allowing users to create, share, and watch vertical video content.
-
A.
VIDO
VIDO is a Canadian research organization at the University of Saskatchewan specializing in vaccine development and infectious disease research for both humans and animals.
-
B.
Vdio
Vdio was an online video-on-demand and streaming service launched by Skype and Rdio co-founder Janus Friis as an attempt to compete with platforms like Netflix.
-
C.
VIDN
VIDN is the four-letter ICAO airport code assigned to Noida International Airport in Uttar Pradesh, India.
-
D.
YouTube Shorts
YouTube Shorts is YouTube’s short-form vertical video platform designed for quick, snackable content similar to TikTok and Instagram Reels.
-
E.
Shorts
Shorts is a surname shared by various individuals, including American football player Cecil Shorts III.
- F. None of above. chosen
Provenance (5 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_69c008d7a9c4819098d647ec47776917 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c067f8758081909c5ce40abf57dd2c |
completed | March 22, 2026, 10:06 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c62d6d906481908b5883bff18ceec8 |
completed | March 27, 2026, 7:10 a.m. |
| NEDg | Description generation | batch_69c62e2072808190a4f2dd262b631c88 |
completed | March 27, 2026, 7:13 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c62f1bbdac8190b0cff9fbcddd68a7 |
completed | March 27, 2026, 7:17 a.m. |
Created at: March 22, 2026, 4:32 p.m.