Triple
T16306445
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jeff Heer |
E395926
|
entity |
| Predicate | coCreatorOf |
P806
|
FINISHED |
| Object | Vega-Lite |
E1205483
|
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: Vega-Lite | Statement: [Jeff Heer, coCreatorOf, Vega-Lite]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vega-Lite Context triple: [Jeff Heer, coCreatorOf, Vega-Lite]
-
A.
Vega-Lite
chosen
Vega-Lite is a high-level grammar of interactive graphics that enables users to concisely create and share data visualizations, developed under the guidance of computer scientist Jeff Heer.
-
B.
Vega visualization grammar
Vega visualization grammar is a declarative language and toolkit for creating, sharing, and reproducing interactive data visualizations on the web.
-
C.
Plotly
Plotly is an interactive, open-source graphing and data visualization library widely used in Python for creating rich, web-based charts and dashboards.
-
D.
Protovis
Protovis is a declarative JavaScript toolkit for creating rich, interactive data visualizations in the web browser.
-
E.
D3.js
D3.js is a powerful JavaScript library for creating dynamic, interactive data visualizations in web browsers using web standards like SVG, HTML, and CSS.
- 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_69d87f23bb088190a16fbb91a1957ea5 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e288d5619081909d0f8157cc487877 |
completed | April 17, 2026, 7:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00260151908190b83f700a1c7c6419 |
completed | May 10, 2026, 6:30 a.m. |
Created at: April 10, 2026, 5:06 a.m.