Triple
T9010523
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Madeleine |
E215457
|
entity |
| Predicate | derivedFrom |
P909
|
FINISHED |
| Object | Magdalene |
E439755
|
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: Magdalene | Statement: [Madeleine, derivedFrom, Magdalene]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Magdalene Context triple: [Madeleine, derivedFrom, Magdalene]
-
A.
Magdalene
chosen
Magdalene is the birth name of the iconic German-American actress and singer Marlene Dietrich, renowned for her roles in classic Hollywood cinema and her distinctive, androgynous style.
-
B.
St Mary Magdalene
St Mary Magdalene is a Christian church dedicated to Mary Magdalene, serving as the parish church for the village of Mulbarton in Norfolk, England.
-
C.
Magdalene Shaw
Magdalene Shaw is a sharp-witted, tough matriarch and career criminal in the Fast & Furious franchise, known as the mother of Deckard and Owen Shaw.
-
D.
Our Lady
Our Lady is a traditional Christian title of reverence for the Virgin Mary, the mother of Jesus.
-
E.
Bernardine
Bernardine is a 1957 musical comedy film starring Pat Boone in one of his early leading screen roles.
- 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_69ca83a2bf088190986ee7a8eb90407d |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc69c1571881908d0b144786b5ee1f |
completed | April 1, 2026, 12:41 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cfdb9dca848190952427bb5712081f |
completed | April 3, 2026, 3:24 p.m. |
Created at: March 30, 2026, 7:06 p.m.