Triple
T8517964
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maddalena |
E201622
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Madeleine |
E215457
|
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: Madeleine | Statement: [Maddalena, hasVariant, Madeleine]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Madeleine Context triple: [Maddalena, hasVariant, Madeleine]
-
A.
Madeleine
chosen
Madeleine is a feminine given name, commonly used in French and English, derived from Magdalene and often associated with literary and cultural figures.
-
B.
Madeleine
Madeleine is a Paris Métro station in central Paris that serves as an interchange between several metro lines, including the automated Line 14.
-
C.
Madelaine
Madelaine is a character in the Danish crime thriller film "The Salvation."
-
D.
Françoise
Françoise is the given name of Louise de La Vallière, a 17th-century French noblewoman best known as a mistress of King Louis XIV.
-
E.
Marie
Marie is a widely used European given name, especially common in French-speaking countries, derived from the Hebrew name Miryam (Mary).
- 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_69ca8321bb44819081b74df0b710276d |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe626787c819087e72dd76b2d9310 |
completed | March 31, 2026, 3:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce88ec84208190ab72c8411e6cc4c2 |
completed | April 2, 2026, 3:19 p.m. |
Created at: March 30, 2026, 6:15 p.m.