Triple
T17672719
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Charlène |
E440563
|
entity |
| Predicate | etymologicalRoot |
P453
|
FINISHED |
| Object | Charles |
—
|
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: Charles | Statement: [Charlène, etymologicalRoot, Charles]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Charles Context triple: [Charlène, etymologicalRoot, Charles]
-
A.
Charles
chosen
Charles is a masculine given name of Germanic origin that has been widely used across Europe and the English-speaking world, borne by numerous historical figures, royalty, and notable individuals.
-
B.
Edward
Edward is a masculine given name of English origin, historically associated with kings of England and notable figures such as U.S. Senator Edward M. Kennedy.
-
C.
Charles Robert
Charles Robert, better known as Charles I of Hungary, was a 14th-century Angevin king who restored royal authority and initiated economic and administrative reforms in medieval Hungary.
-
D.
George
George is the given name of George W. McLaurin, the first African American student admitted to the University of Oklahoma.
-
E.
George
George is the given name of George M. Whitesides, a prominent American chemist known for his influential work in materials science, nanotechnology, and surface chemistry.
- 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_69d8b9e87e18819087104a44dc4dc5b1 |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e46f6aa64081908c9a82128a5a9024 |
completed | April 19, 2026, 6 a.m. |
Created at: April 10, 2026, 10 a.m.