Triple

T13012804
Position Surface form Disambiguated ID Type / Status
Subject Christopher Fairbank E322463 entity
Predicate notableWork P4 FINISHED
Object Luther E38385 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: Luther | Statement: [Christopher Fairbank, notableWork, Luther]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Luther
Context triple: [Christopher Fairbank, notableWork, Luther]
  • A. Luther
    Luther is a masculine given name of Germanic origin, most famously borne by civil rights leader Martin Luther King Jr. and R&B singer Luther Vandross.
  • B. Luther
    Luther is the hyper-intense, overprotective "anger translator" character played by Keegan-Michael Key on the sketch comedy show Key & Peele, best known for comically voicing the unspoken frustrations of President Obama.
  • C. Luther chosen
    Luther is a British psychological crime drama television series starring Idris Elba as a brilliant but troubled detective.
  • D. Luther
    Luther is a common German surname most famously associated with the Protestant Reformer Martin Luther and his family.
  • E. Luther
    Luther is a small town in central Oklahoma, United States, known for its rural character and location along historic Route 66.
  • 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_69d807657e8c8190bd9435ee2f823845 completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69d97ecbb8f4819094d55eb07cb5ad97 completed April 10, 2026, 10:50 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6cbc77f308190b3b47f7a092db434 completed May 3, 2026, 4:15 a.m.
Created at: April 9, 2026, 8:49 p.m.