Triple
T10695315
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Warren Brown |
E252122
|
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: [Warren Brown, notableWork, Luther]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Luther Context triple: [Warren Brown, notableWork, Luther]
-
A.
Luther
chosen
Luther is a British psychological crime drama television series starring Idris Elba as a brilliant but troubled detective.
-
B.
Luther
Luther is a common German surname most famously associated with the Protestant Reformer Martin Luther and his family.
-
C.
Luther
Luther is a central criminal-turned-vampire character in the horror film "From Dusk Till Dawn 2: Texas Blood Money."
-
D.
Luther
Luther is a small town in central Oklahoma, United States, known for its rural character and location along historic Route 66.
-
E.
Luther
Luther is a 1961 stage play by British dramatist John Osborne that dramatizes the life and religious struggles of Protestant Reformation leader Martin Luther.
- 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_69d6aa5bd7c08190a816e733b4045c23 |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d6fd39c3788190bb7cd0acf8b6efdd |
completed | April 9, 2026, 1:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69dbacf1a43c8190869f4a64f9d6a26c |
completed | April 12, 2026, 2:32 p.m. |
Created at: April 8, 2026, 9:11 p.m.