Triple
T17511696
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Crooked Teeth |
E426465
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object | Periscope |
—
|
NE NERFINISHED |
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: Periscope | Statement: [Crooked Teeth, hasPart, Periscope]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Periscope Context triple: [Crooked Teeth, hasPart, Periscope]
-
A.
Periscope
chosen
Periscope was a live video streaming mobile app that allowed users to broadcast and watch real-time video from around the world.
-
B.
Viddy
Viddy was a mobile social video-sharing app that allowed users to create, edit, and share short video clips with an online community.
-
C.
Snapchat Lens Explorer
Snapchat Lens Explorer is a discovery interface within Snapchat that lets users browse, search, and try a wide variety of user-created augmented reality Lenses.
-
D.
Lightbox
Lightbox is a London-based production company known for creating high-profile documentary films and series.
-
E.
Quibi
Quibi was a short-lived mobile-focused streaming platform that specialized in brief, episodic video content designed for on-the-go viewing.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d889dd9164819087b1dc3c9240c870 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e4525c21c88190a9394c4bce006a38 |
completed | April 19, 2026, 3:56 a.m. |
Created at: April 10, 2026, 5:48 a.m.