Triple
T6770965
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ebony |
E155040
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Ebony |
E320737
|
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: Ebony | Statement: [Ebony, name, Ebony]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ebony Context triple: [Ebony, name, Ebony]
-
A.
Ebony
chosen
Ebony is the given first name of Canadian record producer WondaGurl, known for her work with major hip-hop and R&B artists.
-
B.
Nigrita
Nigrita is a small town in northern Greece known for its agricultural production and thermal springs, located within the Serres regional unit of Central Macedonia.
-
C.
A Negra
A Negra is a seminal modernist painting by Brazilian artist Tarsila do Amaral that explores Afro-Brazilian identity through bold forms and vibrant colors.
-
D.
Négrette
Négrette is a dark-skinned French wine grape variety known for producing deeply colored, aromatic red and rosé wines with floral and spicy notes, particularly in the southwest of France.
-
E.
Mahogany
Mahogany is a music producer known for contributing to Mariah Carey's work, including on her album "The Emancipation of Mimi."
- 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_69c68812ef7c819099369f51febb725c |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d2496fa08190895d8b625fb0d699 |
completed | March 27, 2026, 6:54 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c712c46b70819097401afab991c808 |
completed | March 27, 2026, 11:29 p.m. |
Created at: March 27, 2026, 2:13 p.m.