Triple
T9514761
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Wound of Knowledge |
E229494
|
entity |
| Predicate | interprets |
P1044
|
FINISHED |
| Object | Martin Luther |
E8525
|
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: Martin Luther | Statement: [The Wound of Knowledge, interprets, Martin Luther]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Martin Luther Context triple: [The Wound of Knowledge, interprets, Martin Luther]
-
A.
Martin Luther
chosen
Martin Luther was a 16th-century German theologian and key figure of the Protestant Reformation whose teachings challenged Catholic doctrine and reshaped Western Christianity.
-
B.
Martin Franz Luther
Martin Franz Luther was a German Nazi diplomat and SS official who served in the Foreign Office and participated in the administration of the Holocaust.
-
C.
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.
-
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 British psychological crime drama television series starring Idris Elba as a brilliant but troubled detective.
- 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_69ca84777560819084cddd999badc1aa |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd986bfcac8190aa97f8975cb17f6c |
completed | April 1, 2026, 10:13 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d13a44c3c08190a09277737c7a98e0 |
completed | April 4, 2026, 4:20 p.m. |
Created at: March 30, 2026, 7:58 p.m.