Triple
T4452897
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marlene Dietrich |
E97654
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
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.
|
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: [Marlene Dietrich, givenName, Magdalene]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Magdalene Context triple: [Marlene Dietrich, givenName, Magdalene]
-
A.
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.
-
B.
Our Lady
Our Lady is a traditional Christian title of reverence for the Virgin Mary, the mother of Jesus.
-
C.
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.
-
D.
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."
-
E.
Maud
Maud was a Norwegian polar exploration ship used by Roald Amundsen during his Arctic expeditions in the early 20th century.
- 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: [Marlene Dietrich, givenName, Magdalene]
Generated description
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.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Magdalene Target entity description: 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.
-
A.
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.
-
B.
Our Lady
Our Lady is a traditional Christian title of reverence for the Virgin Mary, the mother of Jesus.
-
C.
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.
-
D.
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."
-
E.
Maud
Maud was a Norwegian polar exploration ship used by Roald Amundsen during his Arctic expeditions in the early 20th century.
- F. None of above. chosen
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_69b3454777808190b78aa9047ba1f018 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b355f49dc081908727af81b886c08d |
completed | March 13, 2026, 12:10 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b6138ea77881908381e9ea8ad2b7fe |
completed | March 15, 2026, 2:03 a.m. |
| NEDg | Description generation | batch_69b61465bb4081908a638a908c31c489 |
completed | March 15, 2026, 2:07 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b6156db4a081909054f7db172315e9 |
completed | March 15, 2026, 2:11 a.m. |
Created at: March 12, 2026, 11:33 p.m.