Triple
T905160
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ella Baker |
E19530
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Ella |
E84771
|
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: Ella | Statement: [Ella Baker, givenName, Ella]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ella Context triple: [Ella Baker, givenName, Ella]
-
A.
Ella
chosen
Ella is a feminine given name most famously associated with legendary American jazz singer Ella Fitzgerald.
-
B.
Lulu
Lulu is a common feminine given name or nickname, often used as a diminutive form of names like Louise.
-
C.
Niña
Niña was one of the three ships in Christopher Columbus’s 1492 voyage across the Atlantic, notable for its role in the first European expedition to the Americas.
-
D.
Mya
Mya is a central female character in the romantic comedy film "Think Like a Man," known for following Steve Harvey’s dating advice as she navigates modern relationships.
-
E.
Lady Ella
Lady Ella is the affectionate nickname of Ella Fitzgerald, the legendary American jazz singer renowned for her pure tone, impeccable diction, and virtuosic scat singing.
- 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_69a4939e889c8190ac148b3ac1a7f90b |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4b2caf4088190ab05b22531ecec43 |
completed | March 1, 2026, 9:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a7c7391e6c8190836e8d7e7fdf9c93 |
completed | March 4, 2026, 5:46 a.m. |
Created at: March 1, 2026, 7:39 p.m.