Triple
T8517993
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Malena |
E201623
|
entity |
| Predicate | relatedName |
P3889
|
FINISHED |
| Object | Marlena |
E422738
|
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: Marlena | Statement: [Malena, relatedName, Marlena]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Marlena Context triple: [Malena, relatedName, Marlena]
-
A.
Marlena Evans
chosen
Marlena Evans is a long-running, iconic fictional psychiatrist and central heroine on the American soap opera "Days of Our Lives."
-
B.
Madelyn
Madelyn is a feminine given name, often considered a modern variant of Madeline and commonly used in English-speaking countries.
-
C.
Marlena Rosenbluth
Marlena Rosenbluth is a glamorous circus performer and animal trainer who becomes the central love interest and emotional core of Sara Gruen’s novel "Water for Elephants."
-
D.
Madelaine
Madelaine is a character in the Danish crime thriller film "The Salvation."
-
E.
Adrienne
Adrienne is a feminine given name of French origin, commonly used in English- and French-speaking countries.
- 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_69ca8321bb44819081b74df0b710276d |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe626787c819087e72dd76b2d9310 |
completed | March 31, 2026, 3:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cea84a8a5081909bdcc066e2ba09a4 |
completed | April 2, 2026, 5:32 p.m. |
Created at: March 30, 2026, 6:15 p.m.