Triple
T10089845
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Andrea King |
E215313
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Georgette |
E505869
|
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: Georgette | Statement: [Andrea King, givenName, Georgette]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Georgette Context triple: [Andrea King, givenName, Georgette]
-
A.
Georgette
chosen
Georgette is a comic servant character in Molière’s play "L’École des femmes," known for her earthy wit and role in highlighting the play’s social and gender tensions.
-
B.
Louise
Louise is a feminine given name of French origin, traditionally associated with nobility and widely used in many European and English-speaking countries.
-
C.
Louise
Louise is an opera by French composer Gustave Charpentier, renowned for its realistic portrayal of Parisian working-class life and its influential role in early 20th-century French opera.
-
D.
Madeleine
Madeleine is a feminine given name, commonly used in French and English, derived from Magdalene and often associated with literary and cultural figures.
-
E.
Madeleine
Madeleine is a Paris Métro station in central Paris that serves as an interchange between several metro lines, including the automated Line 14.
- 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_69ca83a1eed081908b2e9580f2ebeea7 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cdd05960008190baecb8e4c9f2461f |
completed | April 2, 2026, 2:11 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d2b69cd26c8190bf4b488377dc0ce1 |
completed | April 5, 2026, 7:23 p.m. |
Created at: March 30, 2026, 9:01 p.m.