Triple
T20870007
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cherry Seaborn |
E513863
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Seaborn |
—
|
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: Seaborn | Statement: [Cherry Seaborn, familyName, Seaborn]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Seaborn Context triple: [Cherry Seaborn, familyName, Seaborn]
-
A.
Seaborn
Seaborn is a Python data visualization library built on top of Matplotlib that provides a high-level interface for creating attractive and informative statistical graphics.
-
B.
Seaborn
chosen
Seaborn is a masculine given name of English origin, historically used in colonial America and associated with individuals such as Seaborn Cotton.
-
C.
Matplotlib
Matplotlib is a widely used Python plotting library for creating static, animated, and interactive visualizations.
-
D.
Plotly
Plotly is an interactive, open-source graphing and data visualization library widely used in Python for creating rich, web-based charts and dashboards.
-
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 (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_69e0b4f675cc8190b4e745225b62eb66 |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c4637ec48190830023d20fb8124c |
completed | April 21, 2026, 12:27 a.m. |
Created at: April 16, 2026, 12:45 p.m.