Triple
T16306443
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jeff Heer |
E395926
|
entity |
| Predicate | coCreatorOf |
P806
|
FINISHED |
| Object | D3.js |
E1205481
|
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: D3.js | Statement: [Jeff Heer, coCreatorOf, D3.js]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: D3.js Context triple: [Jeff Heer, coCreatorOf, D3.js]
-
A.
D3.js
chosen
D3.js is a powerful JavaScript library for creating dynamic, interactive data visualizations in web browsers using web standards like SVG, HTML, and CSS.
-
B.
Protovis
Protovis is a declarative JavaScript toolkit for creating rich, interactive data visualizations in the web browser.
-
C.
d3
d3 is a purpose-built creative hub in Dubai that fosters design, fashion, art, and innovation through studios, galleries, offices, and community events.
-
D.
“D3: Data-Driven Documents”
“D3: Data-Driven Documents” is a popular JavaScript library for creating dynamic, interactive data visualizations in web browsers using web standards like SVG, HTML, and CSS.
-
E.
Vega-Lite
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.
- 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.