Triple
T17903827
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Merle Dandridge |
E447648
|
entity |
| Predicate | playedCharacter |
P1507
|
FINISHED |
| Object | Marlene |
E1295460
|
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: Marlene | Statement: [Merle Dandridge, playedCharacter, Marlene]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Marlene Context triple: [Merle Dandridge, playedCharacter, Marlene]
-
A.
Marlene
Marlene is an energetic and friendly otter who appears as a main supporting character in the animated series "The Penguins of Madagascar."
-
B.
Marlene
Marlene is the ambitious, career-driven protagonist of Caryl Churchill’s play "Top Girls," whose life embodies the tensions between feminism, success, and personal sacrifice.
-
C.
Marlene
chosen
Marlene is the determined and pragmatic leader of the Fireflies resistance group in The Last of Us universe.
-
D.
Marlene
Marlene is a German biographical film directed by Joseph Vilsmaier about the life and career of actress and singer Marlene Dietrich.
-
E.
Marlene
Marlene is a feminine given name of German origin, often considered a blend of Maria and Magdalene and associated with figures like actress Marlene Dietrich.
- 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_69d8b9f6d394819082a6d69fd1e23d2f |
completed | April 10, 2026, 8:51 a.m. |
| NER | Named-entity recognition | batch_69e49e9a9cfc8190879fc36dfdeb562b |
completed | April 19, 2026, 9:21 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a03212921308190984eb0b2d90f3d11 |
completed | May 12, 2026, 12:46 p.m. |
Created at: April 10, 2026, 10:19 a.m.