Triple
T2352485
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cinderella (2015 film) |
E47478
|
entity |
| Predicate | character |
P662
|
FINISHED |
| Object | Ella |
E84771
|
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: Ella | Statement: [Cinderella (2015 film), character, Ella]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ella Context triple: [Cinderella (2015 film), character, Ella]
-
A.
Ella
chosen
Ella is a feminine given name most famously associated with legendary American jazz singer Ella Fitzgerald.
-
B.
Ela
Ela is a feminine given name used in various cultures, often as a short form of names like Eleanor or Elżbieta.
-
C.
Lulu
Lulu is a common feminine given name or nickname, often used as a diminutive form of names like Louise.
-
D.
Anastacia
Anastacia is an American pop singer-songwriter known for her powerful mezzo-soprano voice and international hits in the early 2000s.
-
E.
Niña
Niña was one of the three ships in Christopher Columbus’s 1492 voyage across the Atlantic, notable for its role in the first European expedition to the Americas.
- 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_69a88a1b678c8190bce986922ba60ce0 |
completed | March 4, 2026, 7:38 p.m. |
| NER | Named-entity recognition | batch_69abc6f8ff548190b07505310e2bf0b9 |
completed | March 7, 2026, 6:34 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae9631c9a481909a3051ac06afdca7 |
completed | March 9, 2026, 9:43 a.m. |
Created at: March 4, 2026, 7:54 p.m.