Triple
T6304412
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Magdalene Brahe |
E141336
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Magdalene
Magdalene is a feminine given name of biblical origin, most commonly associated with Mary Magdalene from the New Testament.
|
E439755
|
NE FINISHED |
How this triple was built (4 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: [Magdalene Brahe, givenName, Magdalene]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Magdalene Context triple: [Magdalene Brahe, givenName, Magdalene]
-
A.
Magdalene
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.
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.
-
C.
Our Lady
Our Lady is a traditional Christian title of reverence for the Virgin Mary, the mother of Jesus.
-
D.
Bernardine
Bernardine is a 1957 musical comedy film starring Pat Boone in one of his early leading screen roles.
-
E.
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.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Magdalene Triple: [Magdalene Brahe, givenName, Magdalene]
Generated description
Magdalene is a feminine given name of biblical origin, most commonly associated with Mary Magdalene from the New Testament.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Magdalene Target entity description: Magdalene is a feminine given name of biblical origin, most commonly associated with Mary Magdalene from the New Testament.
-
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.
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.
-
C.
Our Lady
Our Lady is a traditional Christian title of reverence for the Virgin Mary, the mother of Jesus.
-
D.
Bernardine
Bernardine is a 1957 musical comedy film starring Pat Boone in one of his early leading screen roles.
-
E.
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.
- F. None of above.
Provenance (5 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_69c008cf0ad4819095def81e2bd42f9f |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c0645f26a881909d5746151c0843cc |
completed | March 22, 2026, 9:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c5e43e085081908546fa120a43bf84 |
completed | March 27, 2026, 1:58 a.m. |
| NEDg | Description generation | batch_69c5e69033cc8190a4c3f88993620754 |
completed | March 27, 2026, 2:08 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c5e73595288190a8c0696d7c1dc887 |
completed | March 27, 2026, 2:11 a.m. |
Created at: March 22, 2026, 4:28 p.m.