Triple
T20118339
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Melanie |
E490528
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Melanie |
—
|
NE NERFINISHED |
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: Melanie | Statement: [Melanie, name, Melanie]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Melanie Context triple: [Melanie, name, Melanie]
-
A.
Melanie
Melanie is a supporting character in the comedy film "Grumpy Old Men," involved in the small-town relationships and humorous conflicts that drive the story.
-
B.
Melanie
chosen
Melanie is a feminine given name commonly used in various countries and languages.
-
C.
Melanie
Melanie is a central character in Anita Desai’s novel "Fasting, Feasting," whose experiences reflect the constraints and expectations placed on women within a traditional Indian family.
-
D.
Melanie
Melanie is the given first name of British actress Thandiwe Newton, who was previously credited professionally as Thandie Newton.
-
E.
Melanie
Melanie is a character in Simon Gray's stage play "Quartermaine's Terms," which explores the personal lives and quiet tragedies of teachers at a 1960s Cambridge language school.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69da62636cc08190982cc71733a17b8d |
completed | April 11, 2026, 3:01 p.m. |
| NER | Named-entity recognition | batch_69e6673c32bc8190a52875961fbcc5e2 |
completed | April 20, 2026, 5:49 p.m. |
Created at: April 11, 2026, 11:30 p.m.