Triple
T22374537
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TJ Holowaychuk |
E553120
|
entity |
| Predicate | hasGitHubUsername |
P3930
|
FINISHED |
| Object | visionmedia |
—
|
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: visionmedia | Statement: [TJ Holowaychuk, hasGitHubUsername, visionmedia]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: visionmedia Context triple: [TJ Holowaychuk, hasGitHubUsername, visionmedia]
-
A.
visionmedia
chosen
visionmedia is the online alias of TJ Holowaychuk, a prolific open-source developer known for creating popular Node.js and Go libraries and tools.
-
B.
Vision Video
Vision Video is a film and video production company known for producing and distributing visual media content.
-
C.
VIS
VIS is a large-scale European Union database system used to store and exchange visa application and related biometric data among member states’ authorities.
-
D.
VIS
VIS is the IATA airport code for Visalia Municipal Airport in Visalia, California, United States.
-
E.
Wistia
Wistia is a video hosting and marketing platform focused on helping businesses create, manage, and analyze video content for sales and marketing.
- 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_69e11e4c03248190a26a5060ea6973ee |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15806b534819083716c2b090ede42 |
completed | April 29, 2026, 12:59 a.m. |
Created at: April 16, 2026, 8:45 p.m.