Triple
T20840556
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chelsea Tavares |
E513085
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Tavares |
—
|
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: Tavares | Statement: [Chelsea Tavares, familyName, Tavares]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tavares Context triple: [Chelsea Tavares, familyName, Tavares]
-
A.
Tavares
chosen
Tavares is a Portuguese surname borne by numerous notable individuals across fields such as business, sports, and the arts.
-
B.
Conley
Conley is a surname most prominently associated with NBA point guard Mike Conley Jr.
-
C.
Twachtman
Twachtman is the surname of American Impressionist painter John Henry Twachtman, known for his atmospheric landscapes and innovative use of color and composition.
-
D.
Tolan
Tolan is a surname most notably associated with American television producer, writer, and director Peter Tolan.
-
E.
Toland
Toland is a surname most notably associated with Gregg Toland, the pioneering American cinematographer renowned for his innovative deep-focus techniques in films like "Citizen Kane."
- 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_69e0b4cf62a88190bbf92351e9e57259 |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c34b17b88190b3290bd5100ad2ad |
completed | April 21, 2026, 12:22 a.m. |
Created at: April 16, 2026, 12:43 p.m.