Triple
T575535
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | YouTube Stories |
E13753
|
entity |
| Predicate | similarTo |
P4460
|
FINISHED |
| Object | Instagram Stories |
E5329
|
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: Instagram Stories | Statement: [YouTube Stories, similarTo, Instagram Stories]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Instagram Stories Context triple: [YouTube Stories, similarTo, Instagram Stories]
-
A.
YouTube Stories
YouTube Stories was a short-form, ephemeral video format on YouTube that allowed creators to share temporary, mobile-first content similar to Instagram and Snapchat stories.
-
B.
Instagram
chosen
Instagram is a popular photo and video sharing social media platform known for its visual content, stories, and influencer culture.
-
C.
Snapchat
Snapchat is a multimedia messaging and social media app known for its disappearing photos and videos, creative filters, and Stories feature popular among younger users.
-
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.
Periscope
Periscope was a live video streaming mobile app that allowed users to broadcast and watch real-time video from around the world.
- 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_69a4933fa4d88190a7949cc83c08c5c1 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a49b67395c8190a8046ff7debe9d1f |
completed | March 1, 2026, 8:02 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a4ff4db0248190b2b3ca0290467313 |
completed | March 2, 2026, 3:09 a.m. |
Created at: March 1, 2026, 7:33 p.m.