Triple
T7034038
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Joseph Alberic Twisleton-Wykeham-Fiennes |
E163335
|
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: [Joseph Alberic Twisleton-Wykeham-Fiennes, notableWork, Luther]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Luther Context triple: [Joseph Alberic Twisleton-Wykeham-Fiennes, 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
chosen
Luther is a British psychological crime drama television series starring Idris Elba as a brilliant but troubled detective.
-
C.
Luther
Luther is a common German surname most famously associated with the Protestant Reformer Martin Luther and his family.
-
D.
Luther
Luther is a central criminal-turned-vampire character in the horror film "From Dusk Till Dawn 2: Texas Blood Money."
-
E.
Martin Luther
Martin Luther was a 16th-century German theologian and key figure of the Protestant Reformation whose teachings challenged Catholic doctrine and reshaped Western Christianity.
- 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_69c6885d691c81908cf7d31083113886 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6e2118fc88190a0751ca18eafb4a5 |
completed | March 27, 2026, 8:01 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7759c8e408190a7d457e77a44ee26 |
completed | March 28, 2026, 6:30 a.m. |
Created at: March 27, 2026, 2:36 p.m.