Triple
T23101579
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Two Hands |
E576042
|
entity |
| Predicate | productionCompany |
P490
|
FINISHED |
| Object | CML Films |
—
|
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: CML Films | Statement: [Two Hands, productionCompany, CML Films]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: CML Films Context triple: [Two Hands, productionCompany, CML Films]
-
A.
CML Films
chosen
CML Films is a film production company best known for producing Baz Luhrmann’s acclaimed Australian dance film "Strictly Ballroom."
-
B.
Caramel Films
Caramel Films is a film production company known for producing the 2011 British comedy-drama film "Goon."
-
C.
MLD Films
MLD Films is a film production company known for collaborating with other studios, such as Hopscotch, to develop and produce motion picture projects.
-
D.
Cinelou Films
Cinelou Films is an independent American film production company known for producing character-driven dramas such as the 2014 film "Cake."
-
E.
Cineyug Films
Cineyug Films is an Indian film production company known for backing major Bollywood projects and entertainment ventures.
- 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_69e245c060b48190a9bd61a47a16db17 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f18de9fa8c81909fd26ff37173b85b |
completed | April 29, 2026, 4:49 a.m. |
Created at: April 17, 2026, 3:58 p.m.