Triple

T13994356
Position Surface form Disambiguated ID Type / Status
Subject Jizz in My Pants E336655 entity
Predicate hasOnlinePlatform P57 FINISHED
Object YouTube E2481 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: YouTube | Statement: [Jizz in My Pants, hasOnlinePlatform, YouTube]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: YouTube
Context triple: [Jizz in My Pants, hasOnlinePlatform, YouTube]
  • A. YouTube chosen
    YouTube is a global online video-sharing and streaming platform where users can upload, watch, and interact with a vast range of video content.
  • B. Dailymotion
    Dailymotion is a French video-sharing platform that allows users to upload, watch, and share videos, serving as an alternative to sites like YouTube and Vimeo.
  • C. Google Videos
    Google Videos was a video search and hosting service by Google that allowed users to upload, search, and view online video content before being largely superseded by YouTube.
  • D. Yahoo! Video
    Yahoo! Video was Yahoo's early online video hosting and streaming service that later evolved into the broader Yahoo! Screen platform.
  • E. 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.
  • 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_69d81c645c5c8190b1fd16a285a1b78a completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de2eb53f508190855cd69b8061dd77 completed April 14, 2026, 12:10 p.m.
NED1 Entity disambiguation (via context triple) batch_69fbac9a7e8c8190a0fd0cd67ff50741 completed May 6, 2026, 9:03 p.m.
Created at: April 9, 2026, 10:19 p.m.