Triple
T9892886
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ByteDance |
E181496
|
entity |
| Predicate | product |
P490
|
FINISHED |
| Object | CapCut |
E828494
|
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: CapCut | Statement: [ByteDance, product, CapCut]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: CapCut Context triple: [ByteDance, product, CapCut]
-
A.
CapCut
chosen
CapCut is a popular free video editing application known for its easy-to-use tools, effects, and templates for creating short-form and social media videos.
-
B.
Cutcut
Cutcut is a barangay (village-level administrative division) located in Angeles City in the province of Pampanga, Philippines.
-
C.
Clipchamp
Clipchamp is a browser-based video editing and creation platform, now owned by Microsoft and integrated into Windows as a modern replacement for legacy video editing tools.
-
D.
Windows Photos video editor
Windows Photos video editor is a basic, built-in video editing tool for Windows that lets users trim, merge, and enhance videos with simple effects, music, and text.
-
E.
YouTube Shorts
YouTube Shorts is YouTube’s short-form vertical video platform designed for quick, snackable content similar to TikTok and Instagram Reels.
- 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_69ca8283a6708190801af7a25a7ebb9f |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cdb4814a3c8190ab1fd7f755a44508 |
completed | April 2, 2026, 12:12 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d20d888b408190a68ff55d63558478 |
completed | April 5, 2026, 7:21 a.m. |
Created at: March 30, 2026, 8:39 p.m.