Triple
T17340671
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Green Room |
E421056
|
entity |
| Predicate | hasCharacter |
P2308
|
FINISHED |
| Object | Cécilia |
E135814
|
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: Cécilia | Statement: [The Green Room, hasCharacter, Cécilia]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cécilia Context triple: [The Green Room, hasCharacter, Cécilia]
-
A.
Cecilia
"Cecilia" is a popular, upbeat folk-rock song by the American duo Simon & Garfunkel, known for its catchy rhythm and playful lyrics.
-
B.
Cecilia
chosen
Cecilia is a feminine given name of Latin origin, traditionally associated with Saint Cecilia, the patron saint of music.
-
C.
Cécile
Cécile is the sensitive and central protagonist of the French film "Cible émouvante," around whom the story’s emotional and narrative developments revolve.
-
D.
Rosanna
Rosanna is a feminine given name of Latin origin, derived from a combination of "Rose" and "Anna."
-
E.
Rosanna
Rosanna is a residential suburb in Melbourne, Australia, known for its leafy streets, family-friendly atmosphere, and proximity to parklands and public transport.
- 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_69d889d3adc881909319f1edb8d2a956 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e43a15f6488190ad7d489e7391ab12 |
completed | April 19, 2026, 2:12 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a018c588a7081909ab108cb4adfedfe |
completed | May 11, 2026, 7:59 a.m. |
Created at: April 10, 2026, 5:44 a.m.