Triple
T19458936
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bella Zahra Murphy |
E486813
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Bella |
—
|
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: Bella | Statement: [Bella Zahra Murphy, givenName, Bella]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bella Context triple: [Bella Zahra Murphy, givenName, Bella]
-
A.
Bella
Bella is a 2006 independent drama film starring Tammy Blanchard that explores themes of love, redemption, and unexpected family.
-
B.
Bella
Bella is the given name of Australian actress Bella Heathcote, known for her roles in film and television.
-
C.
Bella
Bella is a close friend of William Thacker, the fictional London bookseller portrayed by Hugh Grant in the romantic comedy film "Notting Hill."
-
D.
Bella
chosen
Bella is a feminine given name commonly used in various cultures, often as a diminutive of names like Isabella or Arabella.
-
E.
Bella
Bella is a character from Quentin Blake’s children’s picture book "Zagazoo," which humorously explores the chaos and transformations of childhood.
- 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_69d8e8d86d608190bd199a98d0297f27 |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e633c6c55c8190965ada884f17c800 |
completed | April 20, 2026, 2:10 p.m. |
Created at: April 10, 2026, 1:38 p.m.