Triple
T1769044
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Magdalena |
E38830
|
entity |
| Predicate | derivedFrom |
P909
|
FINISHED |
| Object | Magdalene |
E67733
|
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: [Magdalena, derivedFrom, Magdalene]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Magdalene Context triple: [Magdalena, derivedFrom, Magdalene]
-
A.
Saint Martha
Saint Martha is a New Testament figure, sister of Mary and Lazarus, venerated as a saint for her hospitality and service to Jesus.
-
B.
Dorcas
Dorcas is the young, enigmatic woman whose tragic love affair and death drive the central events and emotional tensions in Toni Morrison's novel "Jazz."
-
C.
Maud
Maud is a feminine given name of Germanic origin, historically borne by European royalty and nobility.
-
D.
Our Lady of Nazareth
Our Lady of Nazareth is a Marian title of the Virgin Mary that emphasizes her life and role in the town of Nazareth, often serving as the patronal dedication of churches and cathedrals.
-
E.
Maria Magdalena Keverich
chosen
Maria Magdalena Keverich was a German woman best known as the mother of the composer Ludwig van Beethoven.
- 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_69a8862e61708190af97b9838cc3f5de |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69aa648d9f2c8190aca4884648a69eb0 |
completed | March 6, 2026, 5:22 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ada991564c81909ae00fdcb47f52af |
completed | March 8, 2026, 4:53 p.m. |
Created at: March 4, 2026, 7:31 p.m.